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Jia J. Exploration on neurobiological mechanisms of the central–peripheral–central closed-loop rehabilitation. Front Cell Neurosci 2022; 16:982881. [PMID: 36119128 PMCID: PMC9479450 DOI: 10.3389/fncel.2022.982881] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 08/12/2022] [Indexed: 11/13/2022] Open
Abstract
Central and peripheral interventions for brain injury rehabilitation have been widely employed. However, as patients’ requirements and expectations for stroke rehabilitation have gradually increased, the limitations of simple central intervention or peripheral intervention in the rehabilitation application of stroke patients’ function have gradually emerged. Studies have suggested that central intervention promotes the activation of functional brain regions and improves neural plasticity, whereas peripheral intervention enhances the positive feedback and input of sensory and motor control modes to the central nervous system, thereby promoting the remodeling of brain function. Based on the model of a central–peripheral–central (CPC) closed loop, the integration of center and peripheral interventions was effectively completed to form “closed-loop” information feedback, which could be applied to specific brain areas or function-related brain regions of patients. Notably, the closed loop can also be extended to central and peripheral immune systems as well as central and peripheral organs such as the brain–gut axis and lung–brain axis. In this review article, the model of CPC closed-loop rehabilitation and the potential neuroimmunological mechanisms of a closed-loop approach will be discussed. Further, we highlight critical questions about the neuroimmunological aspects of the closed-loop technique that merit future research attention.
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Affiliation(s)
- Jie Jia
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
- National Regional Medical Center, Fujian, China
- The First Affiliated Hospital of Fujian Medical University, Fujian, China
- *Correspondence: Jie Jia,
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Fabietti M, Mahmud M, Lotfi A, Kaiser MS. ABOT: an open-source online benchmarking tool for machine learning-based artefact detection and removal methods from neuronal signals. Brain Inform 2022; 9:19. [PMID: 36048345 PMCID: PMC9437165 DOI: 10.1186/s40708-022-00167-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 07/22/2022] [Indexed: 11/10/2022] Open
Abstract
Brain signals are recorded using different techniques to aid an accurate understanding of brain function and to treat its disorders. Untargeted internal and external sources contaminate the acquired signals during the recording process. Often termed as artefacts, these contaminations cause serious hindrances in decoding the recorded signals; hence, they must be removed to facilitate unbiased decision-making for a given investigation. Due to the complex and elusive manifestation of artefacts in neuronal signals, computational techniques serve as powerful tools for their detection and removal. Machine learning (ML) based methods have been successfully applied in this task. Due to ML's popularity, many articles are published every year, making it challenging to find, compare and select the most appropriate method for a given experiment. To this end, this paper presents ABOT (Artefact removal Benchmarking Online Tool) as an online benchmarking tool which allows users to compare existing ML-driven artefact detection and removal methods from the literature. The characteristics and related information about the existing methods have been compiled as a knowledgebase (KB) and presented through a user-friendly interface with interactive plots and tables for users to search it using several criteria. Key characteristics extracted from over 120 articles from the literature have been used in the KB to help compare the specific ML models. To comply with the FAIR (Findable, Accessible, Interoperable and Reusable) principle, the source code and documentation of the toolbox have been made available via an open-access repository.
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Affiliation(s)
- Marcos Fabietti
- Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK
| | - Mufti Mahmud
- Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK.
- Medical Technologies Innovation Facility, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK.
- Computing and Informatics Research Centre, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK.
| | - Ahmad Lotfi
- Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK
| | - M Shamim Kaiser
- Institute of Information Technology, Jahangirnagar University, Dhaka, 1342, Savar, Bangladesh
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Pereira JA, Ray A, Rana M, Silva C, Salinas C, Zamorano F, Irani M, Opazo P, Sitaram R, Ruiz S. A real-time fMRI neurofeedback system for the clinical alleviation of depression with a subject-independent classification of brain states: A proof of principle study. Front Hum Neurosci 2022; 16:933559. [PMID: 36092645 PMCID: PMC9452730 DOI: 10.3389/fnhum.2022.933559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 07/25/2022] [Indexed: 11/13/2022] Open
Abstract
Most clinical neurofeedback studies based on functional magnetic resonance imaging use the patient's own neural activity as feedback. The objective of this study was to create a subject-independent brain state classifier as part of a real-time fMRI neurofeedback (rt-fMRI NF) system that can guide patients with depression in achieving a healthy brain state, and then to examine subsequent clinical changes. In a first step, a brain classifier based on a support vector machine (SVM) was trained from the neural information of happy autobiographical imagery and motor imagery blocks received from a healthy female participant during an MRI session. In the second step, 7 right-handed female patients with mild or moderate depressive symptoms were trained to match their own neural activity with the neural activity corresponding to the “happiness emotional brain state” of the healthy participant. The training (4 training sessions over 2 weeks) was carried out using the rt-fMRI NF system guided by the brain-state classifier we had created. Thus, the informative voxels previously obtained in the first step, using SVM classification and Effect Mapping, were used to classify the Blood-Oxygen-Level Dependent (BOLD) activity of the patients and converted into real-time visual feedback during the neurofeedback training runs. Improvements in the classifier accuracy toward the end of the training were observed in all the patients [Session 4–1 Median = 6.563%; Range = 4.10–27.34; Wilcoxon Test (0), 2-tailed p = 0.031]. Clinical improvement also was observed in a blind standardized clinical evaluation [HDRS CE2-1 Median = 7; Range 2 to 15; Wilcoxon Test (0), 2-tailed p = 0.016], and in self-report assessments [BDI-II CE2-1 Median = 8; Range 1–15; Wilcoxon Test (0), 2-tailed p = 0.031]. In addition, the clinical improvement was still present 10 days after the intervention [BDI-II CE3-2_Median = 0; Range −1 to 2; Wilcoxon Test (0), 2-tailed p = 0.50/ HDRS CE3-2 Median = 0; Range −1 to 2; Wilcoxon Test (0), 2-tailed p = 0.625]. Although the number of participants needs to be increased and a control group included to confirm these findings, the results suggest a novel option for neural modulation and clinical alleviation in depression using noninvasive stimulation technologies.
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Affiliation(s)
- Jaime A. Pereira
- Departamento de Psiquiatría, Facultad de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile
- Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Andreas Ray
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Mohit Rana
- Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Claudio Silva
- Unidad de Imágenes Cuantitativas Avanzadas, Departamento de Imágenes, Facultad de Medicina, Clínica Alemana- Universidad del Desarrollo, Santiago, Chile
| | - Cesar Salinas
- Unidad de Imágenes Cuantitativas Avanzadas, Departamento de Imágenes, Facultad de Medicina, Clínica Alemana- Universidad del Desarrollo, Santiago, Chile
| | - Francisco Zamorano
- Unidad de Imágenes Cuantitativas Avanzadas, Departamento de Imágenes, Facultad de Medicina, Clínica Alemana- Universidad del Desarrollo, Santiago, Chile
- Laboratorio de Neurociencia Social y Neuromodulación, Centro de Investigación en Complejidad Social (neuroCICS), Facultad de Gobierno, Universidad del Desarrollo, Santiago, Chile
| | - Martin Irani
- Departamento de Psiquiatría, Facultad de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Patricia Opazo
- Departamento de Psiquiatría, Facultad de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Ranganatha Sitaram
- Departamento de Psiquiatría, Facultad de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile
- Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, TN, United States
- *Correspondence: Ranganatha Sitaram
| | - Sergio Ruiz
- Departamento de Psiquiatría, Facultad de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile
- Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
- Sergio Ruiz
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Cao L, Wang W, Huang C, Xu Z, Wang H, Jia J, Chen S, Dong Y, Fan C, de Albuquerque VHC. An Effective Fusing Approach by Combining Connectivity Network Pattern and Temporal-Spatial Analysis for EEG-Based BCI Rehabilitation. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2264-2274. [PMID: 35969547 DOI: 10.1109/tnsre.2022.3198434] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Motor-modality-based brain computer interface (BCI) could promote the neural rehabilitation for stroke patients. Temporal-spatial analysis was commonly used for pattern recognition in this task. This paper introduced a novel connectivity network analysis for EEG-based feature selection. The network features of connectivity pattern not only captured the spatial activities responding to motor task, but also mined the interactive pattern among these cerebral regions. Furthermore, the effective combination between temporal-spatial analysis and network analysis was evaluated for improving the performance of BCI classification (81.7%). And the results demonstrated that it could raise the classification accuracies for most of patients (6 of 7 patients). This proposed method was meaningful for developing the effective BCI training program for stroke rehabilitation.
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Yao L, Jiang N, Mrachacz-Kersting N, Zhu X, Farina D, Wang Y. Reducing the Calibration Time in Somatosensory BCI by Using Tactile ERD. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1870-1876. [PMID: 35767500 DOI: 10.1109/tnsre.2022.3184402] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE We propose a tactile-induced-oscillation approach to reduce the calibration time in somatosensory brain-computer interfaces (BCI). METHODS Based on the similarity between tactile induced event-related desynchronization (ERD) and imagined sensation induced ERD activation, we extensively evaluated BCI performance when using a conventional and a novel calibration strategy. In the conventional calibration, the tactile imagined data was used, while in the sensory calibration model sensory stimulation data was used. Subjects were required to sense the tactile stimulus when real tactile was applied to the left or right wrist and were required to perform imagined sensation tasks in the somatosensory BCI paradigm. RESULTS The sensory calibration led to a significantly better performance than the conventional calibration when tested on the same imagined sensation dataset ( [Formula: see text]=10.89, P=0.0038), with an average 5.1% improvement in accuracy. Moreover, the sensory calibration was 39.3% faster in reaching a performance level of above 70% accuracy. CONCLUSION The proposed approach of using tactile ERD from the sensory cortex provides an effective way of reducing the calibration time in a somatosensory BCI system. SIGNIFICANCE The tactile stimulation would be specifically useful before BCI usage, avoiding excessive fatigue when the mental task is difficult to perform. The tactile ERD approach may find BCI applications for patients or users with preserved afferent pathways.
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Remsik AB, van Kan PLE, Gloe S, Gjini K, Williams L, Nair V, Caldera K, Williams JC, Prabhakaran V. BCI-FES With Multimodal Feedback for Motor Recovery Poststroke. Front Hum Neurosci 2022; 16:725715. [PMID: 35874158 PMCID: PMC9296822 DOI: 10.3389/fnhum.2022.725715] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 05/26/2022] [Indexed: 01/31/2023] Open
Abstract
An increasing number of research teams are investigating the efficacy of brain-computer interface (BCI)-mediated interventions for promoting motor recovery following stroke. A growing body of evidence suggests that of the various BCI designs, most effective are those that deliver functional electrical stimulation (FES) of upper extremity (UE) muscles contingent on movement intent. More specifically, BCI-FES interventions utilize algorithms that isolate motor signals-user-generated intent-to-move neural activity recorded from cerebral cortical motor areas-to drive electrical stimulation of individual muscles or muscle synergies. BCI-FES interventions aim to recover sensorimotor function of an impaired extremity by facilitating and/or inducing long-term motor learning-related neuroplastic changes in appropriate control circuitry. We developed a non-invasive, electroencephalogram (EEG)-based BCI-FES system that delivers closed-loop neural activity-triggered electrical stimulation of targeted distal muscles while providing the user with multimodal sensory feedback. This BCI-FES system consists of three components: (1) EEG acquisition and signal processing to extract real-time volitional and task-dependent neural command signals from cerebral cortical motor areas, (2) FES of muscles of the impaired hand contingent on the motor cortical neural command signals, and (3) multimodal sensory feedback associated with performance of the behavioral task, including visual information, linked activation of somatosensory afferents through intact sensorimotor circuits, and electro-tactile stimulation of the tongue. In this report, we describe device parameters and intervention protocols of our BCI-FES system which, combined with standard physical rehabilitation approaches, has proven efficacious in treating UE motor impairment in stroke survivors, regardless of level of impairment and chronicity.
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Affiliation(s)
- Alexander B. Remsik
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- School of Medicine and Public Health, Institute for Clinical and Translational Research, University of Wisconsin–Madison, Madison, WI, United States
- Department of Kinesiology, University of Wisconsin–Madison, Madison, WI, United States
| | - Peter L. E. van Kan
- Department of Kinesiology, University of Wisconsin–Madison, Madison, WI, United States
- Neuroscience Training Program, University of Wisconsin–Madison, Madison, WI, United States
| | - Shawna Gloe
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
| | - Klevest Gjini
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Department of Neurology, University of Wisconsin–Madison, Madison, WI, United States
| | - Leroy Williams
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Department of Educational Psychology, University of Wisconsin–Madison, Madison, WI, United States
| | - Veena Nair
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
| | - Kristin Caldera
- Department of Orthopedics and Rehabilitation, School of Medicine and Public Health, University of Wisconsin–Madison, Madison, WI, United States
| | - Justin C. Williams
- Department of Biomedical Engineering, University of Wisconsin–Madison, Madison, WI, United States
- Department of Neurological Surgery, School of Medicine and Public Health, University of Wisconsin–Madison, Madison, WI, United States
| | - Vivek Prabhakaran
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Neuroscience Training Program, University of Wisconsin–Madison, Madison, WI, United States
- Department of Neurology, University of Wisconsin–Madison, Madison, WI, United States
- Department of Psychiatry, School of Medicine and Public Health, University of Wisconsin–Madison, Madison, WI, United States
- Medical Scientist Training Program, School of Medicine and Public Health, University of Wisconsin–Madison, Madison, WI, United States
- Department of Psychology, University of Wisconsin–Madison, Madison, WI, United States
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McGeady C, Vučković A, Singh Tharu N, Zheng YP, Alam M. Brain-Computer Interface Priming for Cervical Transcutaneous Spinal Cord Stimulation Therapy: An Exploratory Case Study. FRONTIERS IN REHABILITATION SCIENCES 2022; 3:896766. [PMID: 36188944 PMCID: PMC9397879 DOI: 10.3389/fresc.2022.896766] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 06/01/2022] [Indexed: 06/01/2023]
Abstract
Loss of arm and hand function is one of the most devastating consequences of cervical spinal cord injury (SCI). Although some residual functional neurons often pass the site of injury, recovery after SCI is extremely limited. Recent efforts have aimed to augment traditional rehabilitation by combining exercise-based training with techniques such as transcutaneous spinal cord stimulation (tSCS), and movement priming. Such methods have been linked with elevated corticospinal excitability, and enhanced neuroplastic effects following activity-based therapy. In the present study, we investigated the potential for facilitating tSCS-based exercise-training with brain-computer interface (BCI) motor priming. An individual with chronic AIS A cervical SCI with both sensory and motor complete tetraplegia participated in a two-phase cross-over intervention whereby they engaged in 15 sessions of intensive tSCS-mediated hand training for 1 h, 3 times/week, followed by a two week washout period, and a further 15 sessions of tSCS training with bimanual BCI motor priming preceding each session. We found using the Graded Redefined Assessment for Strength, Sensibility, and Prehension that the participant's arm and hand function improved considerably across each phase of the study: from 96/232 points at baseline, to 117/232 after tSCS training alone, and to 131/232 points after BCI priming with tSCS training, reflecting improved strength, sensation, and gross and fine motor skills. Improved motor scores and heightened perception to sharp sensations improved the neurological level of injury from C4 to C5 following training and improvements were generally maintained four weeks after the final training session. Although functional improvements were similar regardless of the presence of BCI priming, there was a moderate improvement of bilateral strength only when priming preceded tSCS training, perhaps suggesting a benefit of motor priming for tSCS training.
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Affiliation(s)
- Ciarán McGeady
- Centre for Rehabilitation Engineering, University of Glasgow, Glasgow, United Kingdom
| | - Aleksandra Vučković
- Centre for Rehabilitation Engineering, University of Glasgow, Glasgow, United Kingdom
| | - Niraj Singh Tharu
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Yong-Ping Zheng
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Monzurul Alam
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
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Bodda S, Diwakar S. Exploring EEG spectral and temporal dynamics underlying a hand grasp movement. PLoS One 2022; 17:e0270366. [PMID: 35737671 PMCID: PMC9223346 DOI: 10.1371/journal.pone.0270366] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 06/08/2022] [Indexed: 11/28/2022] Open
Abstract
For brain-computer interfaces, resolving the differences between pre-movement and movement requires decoding neural ensemble activity in the motor cortex's functional regions and behavioural patterns. Here, we explored the underlying neural activity and mechanisms concerning a grasped motor task by recording electroencephalography (EEG) signals during the execution of hand movements in healthy subjects. The grasped movement included different tasks; reaching the target, grasping the target, lifting the object upwards, and moving the object in the left or right directions. 163 trials of EEG data were acquired from 30 healthy participants who performed the grasped movement tasks. Rhythmic EEG activity was analysed during the premovement (alert task) condition and compared against grasped movement tasks while the arm was moved towards the left or right directions. The short positive to negative deflection that initiated around -0.5ms as a wave before the onset of movement cue can be used as a potential biomarker to differentiate movement initiation and movement. A rebound increment of 14% of beta oscillations and 26% gamma oscillations in the central regions was observed and could be used to distinguish pre-movement and grasped movement tasks. Comparing movement initiation to grasp showed a decrease of 10% in beta oscillations and 13% in gamma oscillations, and there was a rebound increment 4% beta and 3% gamma from grasp to grasped movement. We also investigated the combination MRCPs and spectral estimates of α, β, and γ oscillations as features for machine learning classifiers that could categorize movement conditions. Support vector machines with 3rd order polynomial kernel yielded 70% accuracy. Pruning the ranked features to 5 leaf nodes reduced the error rate by 16%. For decoding grasped movement and in the context of BCI applications, this study identifies potential biomarkers, including the spatio-temporal characteristics of MRCPs, spectral information, and choice of classifiers for optimally distinguishing initiation and grasped movement.
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Affiliation(s)
- Sandeep Bodda
- Amrita Mind Brain Center, Amrita Vishwa Vidyapeetham, Kollam, Kerala, India
| | - Shyam Diwakar
- Amrita Mind Brain Center, Amrita Vishwa Vidyapeetham, Kollam, Kerala, India
- Department of Electronics and Communication Engineering, School of Engineering, Amrita Vishwa Vidyapeetham, Kollam, Kerala, India
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Qin Y, Li M, Li Y, Lu Y, Shi X, Cui G, Zhao H, Yang K. Brain-computer interface training for motor recovery after stroke. Hippokratia 2022. [DOI: 10.1002/14651858.cd015065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Yu Qin
- Evidence-Based Medicine Center, School of Basic Medical Sciences; Lanzhou University; Lanzhou China
- Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province; Lanzhou University; Lanzhou China
| | - Meixuan Li
- Evidence-Based Medicine Center, School of Basic Medical Sciences; Lanzhou University; Lanzhou China
- Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province; Lanzhou University; Lanzhou China
| | - Yanfei Li
- Evidence-Based Medicine Center, School of Basic Medical Sciences; Lanzhou University; Lanzhou China
- Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province; Lanzhou University; Lanzhou China
| | - Yaqin Lu
- Department of Rehabilitation Medicine; Gansu Province Central Hospital; Lanzhou China
| | - Xiue Shi
- Shaanxi Kangfu Hospital; Xi'an China
| | - Gecheng Cui
- Evidence Based Social Science Research Center, School of Public Health; Lanzhou University; Lanzhou China
| | - Haitong Zhao
- Evidence Based Social Science Research Center, School of Public Health; Lanzhou University; Lanzhou China
| | - KeHu Yang
- Evidence-Based Medicine Center, School of Basic Medical Sciences; Lanzhou University; Lanzhou China
- Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province; Lanzhou University; Lanzhou China
- Evidence Based Social Science Research Center, School of Public Health; Lanzhou University; Lanzhou China
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60
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Davis KC, Meschede-Krasa B, Cajigas I, Prins NW, Alver C, Gallo S, Bhatia S, Abel JH, Naeem JA, Fisher L, Raza F, Rifai WR, Morrison M, Ivan ME, Brown EN, Jagid JR, Prasad A. Design-development of an at-home modular brain-computer interface (BCI) platform in a case study of cervical spinal cord injury. J Neuroeng Rehabil 2022; 19:53. [PMID: 35659259 PMCID: PMC9166490 DOI: 10.1186/s12984-022-01026-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Accepted: 05/13/2022] [Indexed: 11/10/2022] Open
Abstract
OBJECTIVE The objective of this study was to develop a portable and modular brain-computer interface (BCI) software platform independent of input and output devices. We implemented this platform in a case study of a subject with cervical spinal cord injury (C5 ASIA A). BACKGROUND BCIs can restore independence for individuals with paralysis by using brain signals to control prosthetics or trigger functional electrical stimulation. Though several studies have successfully implemented this technology in the laboratory and the home, portability, device configuration, and caregiver setup remain challenges that limit deployment to the home environment. Portability is essential for transitioning BCI from the laboratory to the home. METHODS The BCI platform implementation consisted of an Activa PC + S generator with two subdural four-contact electrodes implanted over the dominant left hand-arm region of the sensorimotor cortex, a minicomputer fixed to the back of the subject's wheelchair, a custom mobile phone application, and a mechanical glove as the end effector. To quantify the performance for this at-home implementation of the BCI, we quantified system setup time at home, chronic (14-month) decoding accuracy, hardware and software profiling, and Bluetooth communication latency between the App and the minicomputer. We created a dataset of motor-imagery labeled signals to train a binary motor imagery classifier on a remote computer for online, at-home use. RESULTS Average bluetooth data transmission delay between the minicomputer and mobile App was 23 ± 0.014 ms. The average setup time for the subject's caregiver was 5.6 ± 0.83 min. The average times to acquire and decode neural signals and to send those decoded signals to the end-effector were respectively 404.1 ms and 1.02 ms. The 14-month median accuracy of the trained motor imagery classifier was 87.5 ± 4.71% without retraining. CONCLUSIONS The study presents the feasibility of an at-home BCI system that subjects can seamlessly operate using a friendly mobile user interface, which does not require daily calibration nor the presence of a technical person for at-home setup. The study also describes the portability of the BCI system and the ability to plug-and-play multiple end effectors, providing the end-user the flexibility to choose the end effector to accomplish specific motor tasks for daily needs. Trial registration ClinicalTrials.gov: NCT02564419. First posted on 9/30/2015.
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Affiliation(s)
- Kevin C Davis
- Department of Biomedical Engineering, University of Miami, 1251 Memorial Dr, MEA 204, Coral Gables, Miami, FL, 33146, USA
| | - Benyamin Meschede-Krasa
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Iahn Cajigas
- Department of Neurological Surgery, University of Miami, 1095 NW 14th Terrace, Miami, FL, 33136, USA
| | - Noeline W Prins
- Department of Biomedical Engineering, University of Miami, 1251 Memorial Dr, MEA 204, Coral Gables, Miami, FL, 33146, USA
- Department of Electrical and Information Engineering, University of Ruhuna, Matara, Sri Lanka
| | - Charles Alver
- Department of Biomedical Engineering, University of Miami, 1251 Memorial Dr, MEA 204, Coral Gables, Miami, FL, 33146, USA
| | - Sebastian Gallo
- Department of Biomedical Engineering, University of Miami, 1251 Memorial Dr, MEA 204, Coral Gables, Miami, FL, 33146, USA
| | - Shovan Bhatia
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - John H Abel
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, 02115, USA
| | - Jasim A Naeem
- Department of Biomedical Engineering, University of Miami, 1251 Memorial Dr, MEA 204, Coral Gables, Miami, FL, 33146, USA
| | - Letitia Fisher
- Miami Project to Cure Paralysis, University of Miami, Miami, FL, 33136, USA
| | - Fouzia Raza
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA
| | - Wesley R Rifai
- Department of Biomedical Engineering, University of Miami, 1251 Memorial Dr, MEA 204, Coral Gables, Miami, FL, 33146, USA
| | - Matthew Morrison
- Department of Biomedical Engineering, University of Miami, 1251 Memorial Dr, MEA 204, Coral Gables, Miami, FL, 33146, USA
| | - Michael E Ivan
- Department of Neurological Surgery, University of Miami, 1095 NW 14th Terrace, Miami, FL, 33136, USA
| | - Emery N Brown
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Jonathan R Jagid
- Department of Neurological Surgery, University of Miami, 1095 NW 14th Terrace, Miami, FL, 33136, USA.
- Miami Project to Cure Paralysis, University of Miami, Miami, FL, 33136, USA.
| | - Abhishek Prasad
- Department of Biomedical Engineering, University of Miami, 1251 Memorial Dr, MEA 204, Coral Gables, Miami, FL, 33146, USA.
- Miami Project to Cure Paralysis, University of Miami, Miami, FL, 33136, USA.
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61
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Unmanned Aerial Vehicle for Laser Based Biomedical Sensor Development and Examination of Device Trajectory. SENSORS 2022; 22:s22093413. [PMID: 35591103 PMCID: PMC9102918 DOI: 10.3390/s22093413] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 04/23/2022] [Accepted: 04/25/2022] [Indexed: 12/23/2022]
Abstract
Controller design and signal processing for the control of air-vehicles have gained extreme importance while interacting with humans to form a brain–computer interface. This is because fewer commands need to be mapped into multiple controls. For our anticipated biomedical sensor for breath analysis, it is mandatory to provide medication to the patients on an urgent basis. To address this increasingly tense situation in terms of emergencies, we plan to design an unmanned vehicle that can aid spontaneously to monitor the person’s health, and help the physician spontaneously during the rescue mission. Simultaneously, that must be done in such a computationally efficient algorithm that the minimum amount of energy resources are consumed. For this purpose, we resort to an unmanned logistic air-vehicle which flies from the medical centre to the affected person. After obtaining restricted permission from the regional administration, numerous challenges are identified for this design. The device is able to lift a weight of 2 kg successfully which is required for most emergency medications, while choosing the smallest distance to the destination with the GPS. By recording the movement of the vehicle in numerous directions, the results deviate to a maximum of 2% from theoretical investigations. In this way, our biomedical sensor provides critical information to the physician, who is able to provide medication to the patient urgently. On account of reasonable supply of medicines to the destination in terms of weight and time, this experimentation has been rendered satisfactory by the relevant physicians in the vicinity.
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62
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Malvea A, Babaei F, Boulay C, Sachs A, Park J. Deep brain stimulation for Parkinson’s Disease: A Review and Future Outlook. Biomed Eng Lett 2022; 12:303-316. [PMID: 35892031 PMCID: PMC9308849 DOI: 10.1007/s13534-022-00226-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 12/29/2021] [Accepted: 04/03/2022] [Indexed: 11/30/2022] Open
Abstract
Parkinson's Disease (PD) is a neurodegenerative disorder that manifests as an impairment of motor and non-motor abilities due to a loss of dopamine input to deep brain structures. While there is presently no cure for PD, a variety of pharmacological and surgical therapeutic interventions have been developed to manage PD symptoms. This review explores the past, present and future outlooks of PD treatment, with particular attention paid to deep brain stimulation (DBS), the surgical procedure to deliver DBS, and its limitations. Finally, our group's efforts with respect to brain mapping for DBS targeting will be discussed.
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Affiliation(s)
- Anahita Malvea
- Faculty of Medicine, University of Ottawa, K1H 8M5 Ottawa, ON Canada
| | - Farbod Babaei
- School of Electrical Engineering and Computer Science, University of Ottawa, K1N 6N5 Ottawa, ON Canada
| | - Chadwick Boulay
- The Ottawa Hospital Research Institute, Ottawa, Ontario Canada
- The University of Ottawa Brain and Mind Research Institute, Ottawa, Ontario Canada
| | - Adam Sachs
- The Ottawa Hospital Research Institute, Ottawa, Ontario Canada
- The University of Ottawa Brain and Mind Research Institute, Ottawa, Ontario Canada
- Division of Neurosurgery, Department of Surgery, The Ottawa Hospital, Ottawa, Ontario Canada
| | - Jeongwon Park
- School of Electrical Engineering and Computer Science, University of Ottawa, K1N 6N5 Ottawa, ON Canada
- Department of Electrical and Biomedical Engineering, University of Nevada, 89557 Reno, NV USA
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63
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Wang Y, Yang Z, Ji H, Li J, Liu L, Zhuang J. Cross-Modal Transfer Learning From EEG to Functional Near-Infrared Spectroscopy for Classification Task in Brain-Computer Interface System. Front Psychol 2022; 13:833007. [PMID: 35465540 PMCID: PMC9021696 DOI: 10.3389/fpsyg.2022.833007] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 03/07/2022] [Indexed: 11/13/2022] Open
Abstract
The brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) has received more and more attention due to its vast application potential in emotion recognition. However, the relatively insufficient investigation of the feature extraction algorithms limits its use in practice. In this article, to improve the performance of fNIRS-based BCI, we proposed a method named R-CSP-E, which introduces EEG signals when computing fNIRS signals' features based on transfer learning and ensemble learning theory. In detail, we used the Independent Component Analysis (ICA) algorithm for the correspondence between the sources of the two signals. We then introduced the EEG signals when computing the spatial filter based on a modified Common Spatial Pattern (CSP) algorithm. Experimental results on public datasets show that the proposed method in this paper outperforms traditional methods without transfer. In general, the mean classification accuracy can be increased by up to 5%. To our knowledge, it is an innovation that we tried to apply transfer learning between EEG and fNIRS. Our study's findings not only prove the potential of the transfer learning algorithm in cross-model brain-computer interface, but also offer a new and innovative perspective to research the hybrid brain-computer interface.
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Affiliation(s)
- Yuqing Wang
- Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Collage of Electronic and Information Engineering, Tongji University, Shanghai, China
| | - Zhiqiang Yang
- Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Collage of Electronic and Information Engineering, Tongji University, Shanghai, China
| | - Hongfei Ji
- Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Collage of Electronic and Information Engineering, Tongji University, Shanghai, China
| | - Jie Li
- Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Collage of Electronic and Information Engineering, Tongji University, Shanghai, China
| | - Lingyu Liu
- Department of Neurorehabilitation, Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China
| | - Jie Zhuang
- School of Psychology, Shanghai University of Sport, Shanghai, China
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64
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Peng Y, Wang J, Liu Z, Zhong L, Wen X, Wang P, Gong X, Liu H. The Application of Brain-Computer Interface in Upper Limb Dysfunction After Stroke: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Front Hum Neurosci 2022; 16:798883. [PMID: 35422693 PMCID: PMC9001895 DOI: 10.3389/fnhum.2022.798883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 02/17/2022] [Indexed: 11/13/2022] Open
Abstract
Objective This study aimed to examine the effectiveness and safety of the Brain-computer interface (BCI) in treatment of upper limb dysfunction after stroke. Methods English and Chinese electronic databases were searched up to July 2021. Randomized controlled trials (RCTs) were eligible. The methodological quality was assessed using Cochrane’s risk-of-bias tool. Meta-analysis was performed using RevMan 5.4. Results A total of 488 patients from 16 RCTs were included. The results showed that (1) the meta-analysis of BCI-combined treatment on the improvement of the upper limb function showed statistical significance [standardized mean difference (SMD): 0.53, 95% CI: 0.26–0.80, P < 0.05]; (2) BCI treatment can improve the abilities of daily living of patients after stroke, and the analysis results are statistically significant (SMD: 1.67, 95% CI: 0.61–2.74, P < 0.05); and (3) the BCI-combined therapy was not statistically significant for the analysis of the Modified Ashworth Scale (MAS) (SMD: −0.10, 95% CI: −0.50 to 0.30, P = 0.61). Conclusion The meta-analysis indicates that the BCI therapy or BCI combined with other therapies such as conventional rehabilitation training and motor imagery training can improve upper limb dysfunction after stroke and enhance the quality of daily life.
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Affiliation(s)
- Yang Peng
- Department of Rehabilitation Medicine, Yue Bei People’s Hospital, Shaoguan, China
| | - Jing Wang
- Department of Rehabilitation Medicine, Yue Bei People’s Hospital, Shaoguan, China
| | - Zicai Liu
- School of Rehabilitation, Gannan Medical University, Ganzhou, China
| | - Lida Zhong
- Department of Rehabilitation Medicine, Yue Bei People’s Hospital, Shaoguan, China
| | - Xin Wen
- School of Rehabilitation, Gannan Medical University, Ganzhou, China
| | - Pu Wang
- Department of Rehabilitation Medicine, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
- *Correspondence: Pu Wang,
| | - Xiaoqian Gong
- Yue Bei People’s Hospital, Shaoguan, China
- Xiaoqian Gong,
| | - Huiyu Liu
- Department of Rehabilitation Medicine, Yue Bei People’s Hospital, Shaoguan, China
- Huiyu Liu,
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65
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Jiang Y, Jessee W, Hoyng S, Borhani S, Liu Z, Zhao X, Price LK, High W, Suhl J, Cerel-Suhl S. Sharpening Working Memory With Real-Time Electrophysiological Brain Signals: Which Neurofeedback Paradigms Work? Front Aging Neurosci 2022; 14:780817. [PMID: 35418848 PMCID: PMC8995767 DOI: 10.3389/fnagi.2022.780817] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 02/08/2022] [Indexed: 09/19/2023] Open
Abstract
Growing evidence supports the idea that the ultimate biofeedback is to reward sensory pleasure (e.g., enhanced visual clarity) in real-time to neural circuits that are associated with a desired performance, such as excellent memory retrieval. Neurofeedback is biofeedback that uses real-time sensory reward to brain activity associated with a certain performance (e.g., accurate and fast recall). Working memory is a key component of human intelligence. The challenges are in our current limited understanding of neurocognitive dysfunctions as well as in technical difficulties for closed-loop feedback in true real-time. Here we review recent advancements of real time neurofeedback to improve memory training in healthy young and older adults. With new advancements in neuromarkers of specific neurophysiological functions, neurofeedback training should be better targeted beyond a single frequency approach to include frequency interactions and event-related potentials. Our review confirms the positive trend that neurofeedback training mostly works to improve memory and cognition to some extent in most studies. Yet, the training typically takes multiple weeks with 2-3 sessions per week. We review various neurofeedback reward strategies and outcome measures. A well-known issue in such training is that some people simply do not respond to neurofeedback. Thus, we also review the literature of individual differences in psychological factors e.g., placebo effects and so-called "BCI illiteracy" (Brain Computer Interface illiteracy). We recommend the use of Neural modulation sensitivity or BCI insensitivity in the neurofeedback literature. Future directions include much needed research in mild cognitive impairment, in non-Alzheimer's dementia populations, and neurofeedback using EEG features during resting and sleep for memory enhancement and as sensitive outcome measures.
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Affiliation(s)
- Yang Jiang
- Lexington Veteran Affairs Medical Center, Lexington, KY, United States
- College of Medicine, University of Kentucky, Lexington, KY, United States
| | - William Jessee
- College of Medicine, University of Kentucky, Lexington, KY, United States
| | - Stevie Hoyng
- College of Medicine, University of Kentucky, Lexington, KY, United States
| | - Soheil Borhani
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, Knoxville, TN, United States
| | - Ziming Liu
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, Knoxville, TN, United States
| | - Xiaopeng Zhao
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, Knoxville, TN, United States
| | - Lacey K. Price
- Lexington Veteran Affairs Medical Center, Lexington, KY, United States
| | - Walter High
- New Mexico Veteran Affairs Medical Center, Albuquerque, NM, United States
| | - Jeremiah Suhl
- Lexington Veteran Affairs Medical Center, Lexington, KY, United States
| | - Sylvia Cerel-Suhl
- Lexington Veteran Affairs Medical Center, Lexington, KY, United States
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66
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Fitzsimons K, Murphey TD. Ergodic Shared Control: Closing the Loop on pHRI Based on Information Encoded in Motion. ACM TRANSACTIONS ON HUMAN-ROBOT INTERACTION 2022. [DOI: 10.1145/3526106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Advances in exoskeletons and robot arms have given us increasing opportunities for providing physical support and meaningful feedback in training and rehabilitation settings. However, the chosen control strategies must support motor learning and provide mathematical task definitions that are actionable for the actuation. Typical robot control architectures rely on measuring error from a reference trajectory. In physical human-robot interaction, this leads to low engagement, invariant practice, and few errors, which are not conducive to motor learning. A reliance on reference trajectories means that the task definition is both over-specified—requiring specific timings not critical to task success—and lacking information about normal variability. In this article, we examine a way to define tasks and close the loop using an ergodic measure that quantifies how much information about a task is encoded in the human-robot motion. This measure can capture the natural variability that exists in typical human motion—enabling therapy based on scientific principles of motor learning. We implement an ergodic hybrid shared controller(HSC) on a robotic arm as well as an error-based controller—virtual fixtures—in a timed drawing task. In a study of 24 participants, we compare ergodic HSC with virtual fixtures and find that ergodic HSC leads to improved training outcomes.
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Affiliation(s)
| | - Todd D Murphey
- Department of Mechanical Engineering, Northwestern University, USA
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67
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Ha J, Park S, Im CH. Novel Hybrid Brain-Computer Interface for Virtual Reality Applications Using Steady-State Visual-Evoked Potential-Based Brain-Computer Interface and Electrooculogram-Based Eye Tracking for Increased Information Transfer Rate. Front Neuroinform 2022; 16:758537. [PMID: 35281718 PMCID: PMC8908008 DOI: 10.3389/fninf.2022.758537] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Accepted: 01/27/2022] [Indexed: 11/13/2022] Open
Abstract
Brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have recently attracted increasing attention in virtual reality (VR) applications as a promising tool for controlling virtual objects or generating commands in a "hands-free" manner. Video-oculography (VOG) has been frequently used as a tool to improve BCI performance by identifying the gaze location on the screen, however, current VOG devices are generally too expensive to be embedded in practical low-cost VR head-mounted display (HMD) systems. In this study, we proposed a novel calibration-free hybrid BCI system combining steady-state visual-evoked potential (SSVEP)-based BCI and electrooculogram (EOG)-based eye tracking to increase the information transfer rate (ITR) of a nine-target SSVEP-based BCI in VR environment. Experiments were repeated on three different frequency configurations of pattern-reversal checkerboard stimuli arranged in a 3 × 3 matrix. When a user was staring at one of the nine visual stimuli, the column containing the target stimulus was first identified based on the user's horizontal eye movement direction (left, middle, or right) classified using horizontal EOG recorded from a pair of electrodes that can be readily incorporated with any existing VR-HMD systems. Note that the EOG can be recorded using the same amplifier for recording SSVEP, unlike the VOG system. Then, the target visual stimulus was identified among the three visual stimuli vertically arranged in the selected column using the extension of multivariate synchronization index (EMSI) algorithm, one of the widely used SSVEP detection algorithms. In our experiments with 20 participants wearing a commercial VR-HMD system, it was shown that both the accuracy and ITR of the proposed hybrid BCI were significantly increased compared to those of the traditional SSVEP-based BCI in VR environment.
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Affiliation(s)
- Jisoo Ha
- Department of HY-KIST Bio-Convergence, Hanyang University, Seoul, South Korea
| | - Seonghun Park
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
| | - Chang-Hwan Im
- Department of HY-KIST Bio-Convergence, Hanyang University, Seoul, South Korea
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
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68
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Single-Trial Classification of Error-Related Potentials in People with Motor Disabilities: A Study in Cerebral Palsy, Stroke, and Amputees. SENSORS 2022; 22:s22041676. [PMID: 35214576 PMCID: PMC8879227 DOI: 10.3390/s22041676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 02/17/2022] [Accepted: 02/18/2022] [Indexed: 11/16/2022]
Abstract
Brain-computer interface performance may be reduced over time, but adapting the classifier could reduce this problem. Error-related potentials (ErrPs) could label data for continuous adaptation. However, this has scarcely been investigated in populations with severe motor impairments. The aim of this study was to detect ErrPs from single-trial EEG in offline analysis in participants with cerebral palsy, an amputation, or stroke, and determine how much discriminative information different brain regions hold. Ten participants with cerebral palsy, eight with an amputation, and 25 with a stroke attempted to perform 300-400 wrist and ankle movements while a sham BCI provided feedback on their performance for eliciting ErrPs. Pre-processed EEG epochs were inputted in a multi-layer perceptron artificial neural network. Each brain region was used as input individually (Frontal, Central, Temporal Right, Temporal Left, Parietal, and Occipital), the combination of the Central region with each of the adjacent regions, and all regions combined. The Frontal and Central regions were most important, and adding additional regions only improved performance slightly. The average classification accuracies were 84 ± 4%, 87± 4%, and 85 ± 3% for cerebral palsy, amputation, and stroke participants. In conclusion, ErrPs can be detected in participants with motor impairments; this may have implications for developing adaptive BCIs or automatic error correction.
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69
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Hou Y, Jia S, Lun X, Zhang S, Chen T, Wang F, Lv J. Deep Feature Mining via the Attention-Based Bidirectional Long Short Term Memory Graph Convolutional Neural Network for Human Motor Imagery Recognition. Front Bioeng Biotechnol 2022; 9:706229. [PMID: 35223807 PMCID: PMC8873790 DOI: 10.3389/fbioe.2021.706229] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 12/30/2021] [Indexed: 11/13/2022] Open
Abstract
Recognition accuracy and response time are both critically essential ahead of building the practical electroencephalography (EEG)-based brain–computer interface (BCI). However, recent approaches have compromised either the classification accuracy or the responding time. This paper presents a novel deep learning approach designed toward both remarkably accurate and responsive motor imagery (MI) recognition based on scalp EEG. Bidirectional long short-term memory (BiLSTM) with the attention mechanism is employed, and the graph convolutional neural network (GCN) promotes the decoding performance by cooperating with the topological structure of features, which are estimated from the overall data. Particularly, this method is trained and tested on the short EEG recording with only 0.4 s in length, and the result has shown effective and efficient prediction based on individual and groupwise training, with 98.81% and 94.64% accuracy, respectively, which outperformed all the state-of-the-art studies. The introduced deep feature mining approach can precisely recognize human motion intents from raw and almost-instant EEG signals, which paves the road to translate the EEG-based MI recognition to practical BCI systems.
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Affiliation(s)
- Yimin Hou
- School of Automation Engineering, Northeast Electric Power University, Jilin, China
| | - Shuyue Jia
- School of Computer Science, Northeast Electric Power University, Jilin, China
- *Correspondence: Shuyue Jia,
| | - Xiangmin Lun
- School of Automation Engineering, Northeast Electric Power University, Jilin, China
- College of Mechanical and Electric Engineering, Changchun University of Science and Technology, Changchun, China
| | - Shu Zhang
- School of Computer Science, Northwestern Polytechnical University, Xi’an, China
| | - Tao Chen
- School of Automation Engineering, Northeast Electric Power University, Jilin, China
| | - Fang Wang
- School of Automation Engineering, Northeast Electric Power University, Jilin, China
| | - Jinglei Lv
- School of Biomedical Engineering and Brain and Mind Center, University of Sydney, Sydney, NSW, Australia
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70
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Sun J, Wei M, Luo N, Li Z, Wang H. Euler common spatial patterns for EEG classification. Med Biol Eng Comput 2022; 60:753-767. [PMID: 35064439 DOI: 10.1007/s11517-021-02488-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 11/15/2021] [Indexed: 11/28/2022]
Abstract
The technique of common spatial patterns (CSP) is a widely used method in the field of feature extraction of electroencephalogram (EEG) signals. Motivated by the fact that a cosine distance can enlarge the distance between samples of different classes, we propose the Euler CSP (e-CSP) for the feature extraction of EEG signals, and it is then used for EEG classification. The e-CSP is essentially the conventional CSP with the Euler representation. It includes the following two stages: each sample value is first mapped into a complex space by using the Euler representation, and then the conventional CSP is performed in the Euler space. Thus, the e-CSP is equivalent to applying the Euler representation as a kernel function to the input of the CSP. It is computationally as straightforward as the CSP. However, it extracts more discriminative features from the EEG signals. Extensive experimental results illustrate the discrimination ability of the e-CSP.
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Affiliation(s)
- Jing Sun
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, Jiangsu, People's Republic of China.,Institute of Artificial Intelligence of Hefei Comprehensive National Science Center, Hefei, 230094, Anhui, People's Republic of China
| | - Mengting Wei
- Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, People's Republic of China
| | - Ning Luo
- Institute of Software, Chinese Academy of Sciences, Beijing, 100190, People's Republic of China
| | - Zhanli Li
- College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an, 710054, Shanxi, People's Republic of China
| | - Haixian Wang
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, Jiangsu, People's Republic of China. .,Institute of Artificial Intelligence of Hefei Comprehensive National Science Center, Hefei, 230094, Anhui, People's Republic of China.
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71
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Papadopoulos S, Bonaiuto J, Mattout J. An Impending Paradigm Shift in Motor Imagery Based Brain-Computer Interfaces. Front Neurosci 2022; 15:824759. [PMID: 35095410 PMCID: PMC8789741 DOI: 10.3389/fnins.2021.824759] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 12/21/2021] [Indexed: 01/11/2023] Open
Abstract
The development of reliable assistive devices for patients that suffer from motor impairments following central nervous system lesions remains a major challenge in the field of non-invasive Brain-Computer Interfaces (BCIs). These approaches are predominated by electroencephalography and rely on advanced signal processing and machine learning methods to extract neural correlates of motor activity. However, despite tremendous and still ongoing efforts, their value as effective clinical tools remains limited. We advocate that a rather overlooked research avenue lies in efforts to question neurophysiological markers traditionally targeted in non-invasive motor BCIs. We propose an alternative approach grounded by recent fundamental advances in non-invasive neurophysiology, specifically subject-specific feature extraction of sensorimotor bursts of activity recorded via (possibly magnetoencephalography-optimized) electroencephalography. This path holds promise in overcoming a significant proportion of existing limitations, and could foster the wider adoption of online BCIs in rehabilitation protocols.
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Affiliation(s)
- Sotirios Papadopoulos
- University Lyon 1, Lyon, France
- Lyon Neuroscience Research Center, CRNL, INSERM, U1028, CNRS, UMR 5292, Lyon, France
- Institut des Sciences Cognitives Marc Jeannerod, CNRS, UMR 5229, Bron, France
- *Correspondence: Sotirios Papadopoulos,
| | - James Bonaiuto
- University Lyon 1, Lyon, France
- Institut des Sciences Cognitives Marc Jeannerod, CNRS, UMR 5229, Bron, France
| | - Jérémie Mattout
- University Lyon 1, Lyon, France
- Lyon Neuroscience Research Center, CRNL, INSERM, U1028, CNRS, UMR 5292, Lyon, France
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72
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Wang CH, Tsai KY. Optimization of machine learning method combined with brain-computer interface rehabilitation system. J Phys Ther Sci 2022; 34:379-385. [PMID: 35527849 PMCID: PMC9057683 DOI: 10.1589/jpts.34.379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 02/09/2022] [Indexed: 11/30/2022] Open
Abstract
[Purpose] Stroke patients are unable to move on their own and must be rehabilitated to
allow the nervous system to trigger and restore its function. Traditional practice is to
use electrode caps to extract brain wave features and combine them with assistive devices.
However, there are problems that the electrode cap is not easy to wear, and the potential
recognition is not good, and different extraction methods will affect the accuracy of the
Brain-Computer Interfaces (BCI), which still has room for improvement. [Participants and
Methods] The brainwave headphones used in this experiment do not must a conductive gel to
get a good EEG for neural induction and drive the upper limb rehabilitation robot. Next, 8
stroke patients and 200 normal participants were invited for a 4-week rehabilitation
training. The effectiveness of the training was determined using Fast Fourier Transform
(FFT), Magnitude squared coherence (MSC) feature extraction methods, and
five machine learning techniques that induced flicker frequencies. [Results] The results
show that the optimal steady-state visual evoked flicker frequency is 6 Hz, and the
identification rate of FFT is about 5.2% higher than that of the MSC method. Using an
optimized model for different feature extraction methods can improve the recognition rate
by 1.3%–9.1%. [Conclusion] The images based on Fugl-Meyer Assessment (FMA), Modified
Ashworth Scale (MAS) index improvement, and functional Magnetic Resonance
Imaging (fMRI) show that the sensory region of brain movement has become a
concentrated activation phenomenon. Besides strengthening the feature extraction method
also lets the elbow has an obvious recovery effect.
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Affiliation(s)
- Chi-Hung Wang
- Department of Electronic Engineering, National Taipei University of Technology, Taiwan
| | - Kuo-Yu Tsai
- Department of Information Engineering and Computer Science, Feng Chia University: No. 100, Sec. 1, Wenhwa Rd., Seatwen, Taichung city 407, Taiwan
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73
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Yuan Z, Peng Y, Wang L, Song S, Chen S, Yang L, Liu H, Wang H, Shi G, Han C, Cammon JA, Zhang Y, Qiao J, Wang G. Effect of BCI-Controlled Pedaling Training System With Multiple Modalities of Feedback on Motor and Cognitive Function Rehabilitation of Early Subacute Stroke Patients. IEEE Trans Neural Syst Rehabil Eng 2021; 29:2569-2577. [PMID: 34871175 DOI: 10.1109/tnsre.2021.3132944] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Brain-computer interfaces (BCIs) are currently integrated into traditional rehabilitation interventions after stroke. Although BCIs bring many benefits to the rehabilitation process, their effects are limited since many patients cannot concentrate during training. Despite this outcome post-stroke motor-attention dual-task training using BCIs has remained mostly unexplored. This study was a randomized placebo-controlled blinded-endpoint clinical trial to investigate the effects of a BCI-controlled pedaling training system (BCI-PT) on the motor and cognitive function of stroke patients during rehabilitation. A total of 30 early subacute ischemic stroke patients with hemiplegia and cognitive impairment were randomly assigned to the BCI-PT or traditional pedaling training. We used single-channel Fp1 to collect electroencephalography data and analyze the attention index. The BCI-PT system timely provided visual, auditory, and somatosensory feedback to enhance the patient's participation to pedaling based on the real-time attention index. After 24 training sessions, the attention index of the experimental group was significantly higher than that of the control group. The lower limbs motor function (FMA-L) increased by an average of 4.5 points in the BCI-PT group and 2.1 points in the control group (P = 0.022) after treatments. The difference was still significant after adjusting for the baseline indicators ( β = 2.41 , 95%CI: 0.48-4.34, P = 0.024). We found that BCI-PT significantly improved the patient's lower limb motor function by increasing the patient's participation. (clinicaltrials.gov: NCT04612426).
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Jorajuría T, Jamshidi Idaji M, İşcan Z, Gómez M, Nikulin VV, Vidaurre C. Oscillatory Source Tensor Discriminant Analysis (OSTDA): A regularized tensor pipeline for SSVEP-based BCI systems. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.07.103] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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75
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Technology-Based Neurorehabilitation in Parkinson’s Disease—A Narrative Review. CLINICAL AND TRANSLATIONAL NEUROSCIENCE 2021. [DOI: 10.3390/ctn5030023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
This narrative review provides a brief overview of the current literature on technology-based interventions for the neurorehabilitation of persons with Parkinson’s disease (PD). The role of brain–computer interfaces, exergaming/virtual-reality-based exercises, robot-assisted therapies and wearables is discussed. It is expected that technology-based neurorehabilitation will gain importance in the management of PD patients, although it is often not clear yet whether this approach is superior to conventional therapies. High-intensity technology-based neurorehabilitation may hold promise with respect to neuroprotective or neurorestorative actions in PD. Overall, more research is required in order to obtain more data on the feasibility, efficacy and safety of technology-based neurorehabilitation in persons with PD.
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76
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Xie J, Jiang L, Li Y, Chen B, Li F, Jiang Y, Gao D, Deng L, Lv X, Ma X, Yin G, Yao D, Xu P. Rehabilitation of motor function in children with cerebral palsy based on motor imagery. Cogn Neurodyn 2021; 15:939-948. [PMID: 34790263 DOI: 10.1007/s11571-021-09672-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 12/29/2020] [Accepted: 03/02/2021] [Indexed: 11/25/2022] Open
Abstract
To promote the rehabilitation of motor function in children with cerebral palsy (CP), we developed motor imagery (MI) based training system to assist their motor rehabilitation. Eighteen CP children, ten in short- and eight in long-term rehabilitation, participated in our study. In short-term rehabilitation, every 2 days, the MI datasets were collected; whereas the duration of two adjacency MI experiments was ten days in the long-term protocol. Meanwhile, within two adjacency experiments, CP children were requested to daily rehabilitate the motor function based on our system for 30 min. In both strategies, the promoted motor information processing was observed. In terms of the relative signal power spectra, a main effect of time was revealed, as the promoted power spectra were found for the last time of MI recording, compared to that of the first one, which first validated the effectiveness of our intervention. Moreover, as for network efficiency related to the motor information processing, compared to the first MI, the increased network properties were found for the last MI, especially in long-term rehabilitation in which CP children experienced a more obvious efficiency promotion. These findings did validate that our MI-based rehabilitation system has the potential for CP children to assist their motor rehabilitation.
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Affiliation(s)
- Jiaxin Xie
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Lin Jiang
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Yanan Li
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Baodan Chen
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Fali Li
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Yuanling Jiang
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Dongrui Gao
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 611731 China
| | - Lili Deng
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - XuLin Lv
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - XianKun Ma
- Sichuan Rehabilitation Hospital, Chengdu, China
| | - Gang Yin
- School of Medicine, Sichuan Cancer Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, China
- No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731 Sichuan China
| | - Dezhong Yao
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731 China
- No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731 Sichuan China
| | - Peng Xu
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731 China
- No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731 Sichuan China
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77
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Remsik AB, Gjini K, Williams L, van Kan PLE, Gloe S, Bjorklund E, Rivera CA, Romero S, Young BM, Nair VA, Caldera KE, Williams JC, Prabhakaran V. Ipsilesional Mu Rhythm Desynchronization Correlates With Improvements in Affected Hand Grip Strength and Functional Connectivity in Sensorimotor Cortices Following BCI-FES Intervention for Upper Extremity in Stroke Survivors. Front Hum Neurosci 2021; 15:725645. [PMID: 34776902 PMCID: PMC8581197 DOI: 10.3389/fnhum.2021.725645] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 10/01/2021] [Indexed: 12/13/2022] Open
Abstract
Stroke is a leading cause of acquired long-term upper extremity motor disability. Current standard of care trajectories fail to deliver sufficient motor rehabilitation to stroke survivors. Recent research suggests that use of brain-computer interface (BCI) devices improves motor function in stroke survivors, regardless of stroke severity and chronicity, and may induce and/or facilitate neuroplastic changes associated with motor rehabilitation. The present sub analyses of ongoing crossover-controlled trial NCT02098265 examine first whether, during movements of the affected hand compared to rest, ipsilesional Mu rhythm desynchronization of cerebral cortical sensorimotor areas [Brodmann’s areas (BA) 1-7] is localized and tracks with changes in grip force strength. Secondly, we test the hypothesis that BCI intervention results in changes in frequency-specific directional flow of information transmission (direct path functional connectivity) in BA 1-7 by measuring changes in isolated effective coherence (iCoh) between cerebral cortical sensorimotor areas thought to relate to electrophysiological signatures of motor actions and motor learning. A sample of 16 stroke survivors with right hemisphere lesions (left hand motor impairment), received a maximum of 18–30 h of BCI intervention. Electroencephalograms were recorded during intervention sessions while outcome measures of motor function and capacity were assessed at baseline and completion of intervention. Greater desynchronization of Mu rhythm, during movements of the impaired hand compared to rest, were primarily localized to ipsilesional sensorimotor cortices (BA 1-7). In addition, increased Mu desynchronization in the ipsilesional primary motor cortex, Post vs. Pre BCI intervention, correlated significantly with improvements in hand function as assessed by grip force measurements. Moreover, the results show a significant change in the direction of causal information flow, as measured by iCoh, toward the ipsilesional motor (BA 4) and ipsilesional premotor cortices (BA 6) during BCI intervention. Significant iCoh increases from ipsilesional BA 4 to ipsilesional BA 6 were observed in both Mu [8–12 Hz] and Beta [18–26 Hz] frequency ranges. In summary, the present results are indicative of improvements in motor capacity and behavior, and they are consistent with the view that BCI-FES intervention improves functional motor capacity of the ipsilesional hemisphere and the impaired hand.
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Affiliation(s)
- Alexander B Remsik
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Kinesiology, University of Wisconsin-Madison, Madison, WI, United States.,Institute for Clinical and Translational Research, University of Wisconsin-Madison, Madison, WI, United States
| | - Klevest Gjini
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States
| | - Leroy Williams
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Educational Psychology, University of Wisconsin-Madison, Madison, WI, United States.,Center for Women's Health Research, University of Wisconsin-Madison, Madison, WI, United States
| | - Peter L E van Kan
- Department of Kinesiology, University of Wisconsin-Madison, Madison, WI, United States.,Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Shawna Gloe
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Erik Bjorklund
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States.,Clinical Neuroengineering Training Program, University of Wisconsin-Madison, Madison, WI, United States
| | - Cameron A Rivera
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Sophia Romero
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Brittany M Young
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Kinesiology, University of Wisconsin-Madison, Madison, WI, United States.,Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States.,Clinical Neuroengineering Training Program, University of Wisconsin-Madison, Madison, WI, United States.,Medical Scientist Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Veena A Nair
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Kristin E Caldera
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Justin C Williams
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States.,Department of Neurological Surgery, University of Wisconsin-Madison, Madison, WI, United States
| | - Vivek Prabhakaran
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States.,Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States.,Medical Scientist Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States.,Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States.,Department of Psychology, University of Wisconsin-Madison, Madison, WI, United States
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78
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Vargas P, Sitaram R, Sepúlveda P, Montalba C, Rana M, Torres R, Tejos C, Ruiz S. Weighted neurofeedback facilitates greater self-regulation of functional connectivity between the primary motor area and cerebellum. J Neural Eng 2021; 18. [PMID: 34587606 DOI: 10.1088/1741-2552/ac2b7e] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Accepted: 09/29/2021] [Indexed: 11/12/2022]
Abstract
Objective.Brain-computer interface (BCI) is a tool that can be used to train brain self-regulation and influence specific activity patterns, including functional connectivity, through neurofeedback. The functional connectivity of the primary motor area (M1) and cerebellum play a critical role in motor recovery after a brain injury, such as stroke. The objective of this study was to determine the feasibility of achieving control of the functional connectivity between M1 and the cerebellum in healthy subjects. Additionally, we aimed to compare the brain self-regulation of two different feedback modalities and their effects on motor performance.Approach.Nine subjects were trained with a real-time functional magnetic resonance imaging BCI system. Two groups were conformed: equal feedback group (EFG), which received neurofeedback that weighted the contribution of both regions of interest (ROIs) equally, and weighted feedback group (WFG) that weighted each ROI differentially (30% cerebellum; 70% M1). The magnitude of the brain activity induced by self-regulation was evaluated with the blood-oxygen-level-dependent (BOLD) percent change (BPC). Functional connectivity was assessed using temporal correlations between the BOLD signal of both ROIs. A finger-tapping task was included to evaluate the effect of brain self-regulation on motor performance.Main results.A comparison between the feedback modalities showed that WFG achieved significantly higher BPC in M1 than EFG. The functional connectivity between ROIs during up-regulation in WFG was significantly higher than EFG. In general, both groups showed better tapping speed in the third session compared to the first. For WFG, there were significant correlations between functional connectivity and tapping speed.Significance.The results show that it is possible to train healthy individuals to control M1-cerebellum functional connectivity with rtfMRI-BCI. Besides, it is also possible to use a weighted feedback approach to facilitate a higher activity of one region over another.
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Affiliation(s)
- Patricia Vargas
- Interdisciplinary Center for Neuroscience, Department of Psychiatry, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.,Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile.,Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Ranganatha Sitaram
- Interdisciplinary Center for Neuroscience, Department of Psychiatry, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.,Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile.,Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile.,Multimodal Functional Brain Imaging Hub, St. Jude Children's Research Hospital, Memphis, TN, United States of America
| | - Pradyumna Sepúlveda
- Institute of Cognitive Neuroscience (ICN), University College London, London, England
| | - Cristian Montalba
- Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Mohit Rana
- Interdisciplinary Center for Neuroscience, Department of Psychiatry, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.,Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Rafael Torres
- Interdisciplinary Center for Neuroscience, Department of Psychiatry, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Cristián Tejos
- Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile.,Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Sergio Ruiz
- Interdisciplinary Center for Neuroscience, Department of Psychiatry, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.,Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
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79
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Colamarino E, de Seta V, Masciullo M, Cincotti F, Mattia D, Pichiorri F, Toppi J. Corticomuscular and Intermuscular Coupling in Simple Hand Movements to Enable a Hybrid Brain-Computer Interface. Int J Neural Syst 2021; 31:2150052. [PMID: 34590990 DOI: 10.1142/s0129065721500520] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Hybrid Brain-Computer Interfaces (BCIs) for upper limb rehabilitation after stroke should enable the reinforcement of "more normal" brain and muscular activity. Here, we propose the combination of corticomuscular coherence (CMC) and intermuscular coherence (IMC) as control features for a novel hybrid BCI for rehabilitation purposes. Multiple electroencephalographic (EEG) signals and surface electromyography (EMG) from 5 muscles per side were collected in 20 healthy participants performing finger extension (Ext) and grasping (Grasp) with both dominant and non-dominant hand. Grand average of CMC and IMC patterns showed a bilateral sensorimotor area as well as multiple muscles involvement. CMC and IMC values were used as features to classify each task versus rest and Ext versus Grasp. We demonstrated that a combination of CMC and IMC features allows for classification of both movements versus rest with better performance (Area Under the receiver operating characteristic Curve, AUC) for the Ext movement (0.97) with respect to Grasp (0.88). Classification of Ext versus Grasp also showed high performances (0.99). All in all, these preliminary findings indicate that the combination of CMC and IMC could provide for a comprehensive framework for simple hand movements to eventually be employed in a hybrid BCI system for post-stroke rehabilitation.
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Affiliation(s)
- Emma Colamarino
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto 25, Rome 00185, Italy.,Fondazione Santa Lucia IRCCS, Via Ardeatina 306-354, Rome 00179, Italy
| | - Valeria de Seta
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto 25, Rome 00185, Italy.,Fondazione Santa Lucia IRCCS, Via Ardeatina 306-354, Rome 00179, Italy
| | | | - Febo Cincotti
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto 25, Rome 00185, Italy.,Fondazione Santa Lucia IRCCS, Via Ardeatina 306-354, Rome 00179, Italy
| | - Donatella Mattia
- Fondazione Santa Lucia IRCCS, Via Ardeatina 306-354, Rome 00179, Italy
| | | | - Jlenia Toppi
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto 25, Rome 00185, Italy.,Fondazione Santa Lucia IRCCS, Via Ardeatina 306-354, Rome 00179, Italy
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80
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Fahimi F, Dosen S, Ang KK, Mrachacz-Kersting N, Guan C. Generative Adversarial Networks-Based Data Augmentation for Brain-Computer Interface. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021; 32:4039-4051. [PMID: 32841127 DOI: 10.1109/tnnls.2020.3016666] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The performance of a classifier in a brain-computer interface (BCI) system is highly dependent on the quality and quantity of training data. Typically, the training data are collected in a laboratory where the users perform tasks in a controlled environment. However, users' attention may be diverted in real-life BCI applications and this may decrease the performance of the classifier. To improve the robustness of the classifier, additional data can be acquired in such conditions, but it is not practical to record electroencephalogram (EEG) data over several long calibration sessions. A potentially time- and cost-efficient solution is artificial data generation. Hence, in this study, we proposed a framework based on the deep convolutional generative adversarial networks (DCGANs) for generating artificial EEG to augment the training set in order to improve the performance of a BCI classifier. To make a comparative investigation, we designed a motor task experiment with diverted and focused attention conditions. We used an end-to-end deep convolutional neural network for classification between movement intention and rest using the data from 14 subjects. The results from the leave-one subject-out (LOO) classification yielded baseline accuracies of 73.04% for diverted attention and 80.09% for focused attention without data augmentation. Using the proposed DCGANs-based framework for augmentation, the results yielded a significant improvement of 7.32% for diverted attention ( ) and 5.45% for focused attention ( ). In addition, we implemented the method on the data set IVa from BCI competition III to distinguish different motor imagery tasks. The proposed method increased the accuracy by 3.57% ( ). This study shows that using GANs for EEG augmentation can significantly improve BCI performance, especially in real-life applications, whereby users' attention may be diverted.
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81
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Jovanovic LI, Popovic MR, Marquez-Chin C. Characterizing the stimulation interference in electroencephalographic signals during brain-computer interface-controlled functional electrical stimulation therapy. Artif Organs 2021; 46:398-411. [PMID: 34460942 DOI: 10.1111/aor.14059] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 07/23/2021] [Accepted: 08/17/2021] [Indexed: 01/08/2023]
Abstract
INTRODUCTION The integration of brain-computer interface (BCI) and functional electrical stimulation (FES) has brought about a new rehabilitation strategy: BCI-controlled FES therapy or BCI-FEST. During BCI-FEST, the stimulation is triggered by the patient's brain activity, often monitored using electroencephalography (EEG). Several studies have demonstrated that BCI-FEST can improve voluntary arm and hand function after an injury, but few studies have investigated the FES interference in EEG signals during BCI-FEST. In this study, we evaluated the effectiveness of band-pass filters, used to extract the BCI-relevant EEG components, in simultaneously reducing stimulation interference. METHODS We used EEG data from eight participants recorded during BCI-FEST. Additionally, we separately recorded the FES signal generated by the stimulator to estimate the spectral components of the FES interference, and extract the noise in time domain. Finally, we calculated signal-to-noise ratio (SNR) values before and after band-pass filtering, for two types of movements practiced during BCI-FEST: reaching and grasping. RESULTS The SNR values were greater after filtering across all participants for both movement types. For reaching movements, mean SNR values increased between 1.31 dB and 36.3 dB. Similarly, for grasping movements, mean SNR values increased between 2.82 dB and 40.16 dB, after filtering. CONCLUSIONS Band-pass filters, used to isolate EEG frequency bands for BCI application, were also effective in reducing stimulation interference. In addition, we provide a general algorithm that can be used in future studies to estimate the frequencies of FES interference as a function of the selected stimulation pulse frequency, FSTIM , and the EEG sampling rate, FS .
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Affiliation(s)
- Lazar I Jovanovic
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.,The KITE Research Institute, Toronto Rehabilitation Institute-University Health Network, Toronto, Ontario, Canada.,CRANIA, University Health Network, Toronto, Ontario, Canada
| | - Milos R Popovic
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.,The KITE Research Institute, Toronto Rehabilitation Institute-University Health Network, Toronto, Ontario, Canada.,CRANIA, University Health Network, Toronto, Ontario, Canada
| | - Cesar Marquez-Chin
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.,The KITE Research Institute, Toronto Rehabilitation Institute-University Health Network, Toronto, Ontario, Canada.,CRANIA, University Health Network, Toronto, Ontario, Canada
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82
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Lashgari E, Ott J, Connelly A, Baldi P, Maoz U. An end-to-end CNN with attentional mechanism applied to raw EEG in a BCI classification task. J Neural Eng 2021; 18. [PMID: 34352734 DOI: 10.1088/1741-2552/ac1ade] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 08/05/2021] [Indexed: 11/12/2022]
Abstract
Objective.Motor-imagery (MI) classification base on electroencephalography (EEG) has been long studied in neuroscience and more recently widely used in healthcare applications such as mobile assistive robots and neurorehabilitation. In particular, EEG-based MI classification methods that rely on convolutional neural networks (CNNs) have achieved relatively high classification accuracy. However, naively training CNNs to classify raw EEG data from all channels, especially for high-density EEG, is computationally demanding and requires huge training sets. It often also introduces many irrelevant input features, making it difficult for the CNN to extract the informative ones. This problem is compounded by a dearth of training data, which is particularly acute for MI tasks, because these are cognitively demanding and thus fatigue inducing.Approach.To address these issues, we proposed an end-to-end CNN-based neural network with attentional mechanism together with different data augmentation (DA) techniques. We tested it on two benchmark MI datasets, brain-computer interface (BCI) competition IV 2a and 2b. In addition, we collected a new dataset, recorded using high-density EEG, and containing both MI and motor execution (ME) tasks, which we share with the community.Main results.Our proposed neural-network architecture outperformed all state-of-the-art methods that we found in the literature, with and without DA, reaching an average classification accuracy of 93.6% and 87.83% on BCI 2a and 2b, respectively. We also directly compare decoding of MI and ME tasks. Focusing on MI classification, we find optimal channel configurations and the best DA techniques as well as investigate combining data across participants and the role of transfer learning.Significance.Our proposed approach improves the classification accuracy for MI in the benchmark datasets. In addition, collecting our own dataset enables us to compare MI and ME and investigate various aspects of EEG decoding critical for neuroscience and BCI.
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Affiliation(s)
- Elnaz Lashgari
- Schmid College of Science and Technology, Chapman University, Orange, CA, United States of America.,Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, Orange, CA, United States of America
| | - Jordan Ott
- Department of Computer Science, University of California, Irvine, CA, United States of America
| | - Akima Connelly
- Schmid College of Science and Technology, Chapman University, Orange, CA, United States of America.,Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, Orange, CA, United States of America
| | - Pierre Baldi
- Department of Computer Science, University of California, Irvine, CA, United States of America.,Center for Machine Learning and Intelligent Systems, University of California Irvine, Irvine, CA, United States of America.,Institute for Genomics and Bioinformatics, University of California Irvine, Irvine, CA, United States of America
| | - Uri Maoz
- Schmid College of Science and Technology, Chapman University, Orange, CA, United States of America.,Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, Orange, CA, United States of America.,Computational Neuroscience and Psychology, Crean College of Health and Behavioral Sciences, Chapman University, Orange, CA, United States of America.,Fowler School of Engineering, Chapman University, Orange, CA, United States of America.,Anderson School of Management, University of California Los Angeles, Los Angeles, CA, United States of America.,Biology and Bioengineering, California Institute of Technology, Pasadena, CA, United States of America
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83
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Ravindran A, Rieke JD, Zapata JDA, White KD, Matarasso A, Yusufali MM, Rana M, Gunduz A, Modarres M, Sitaram R, Daly JJ. Four methods of brain pattern analyses of fMRI signals associated with wrist extension versus wrist flexion studied for potential use in future motor learning BCI. PLoS One 2021; 16:e0254338. [PMID: 34403422 PMCID: PMC8370644 DOI: 10.1371/journal.pone.0254338] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 06/24/2021] [Indexed: 11/21/2022] Open
Abstract
OBJECTIVE In stroke survivors, a treatment-resistant problem is inability to volitionally differentiate upper limb wrist extension versus flexion. When one intends to extend the wrist, the opposite occurs, wrist flexion, rendering the limb non-functional. Conventional therapeutic approaches have had limited success in achieving functional recovery of patients with chronic and severe upper extremity impairments. Functional magnetic resonance imaging (fMRI) neurofeedback is an emerging strategy that has shown potential for stroke rehabilitation. There is a lack of information regarding unique blood-oxygenation-level dependent (BOLD) cortical activations uniquely controlling execution of wrist extension versus uniquely controlling wrist flexion. Therefore, a first step in providing accurate neural feedback and training to the stroke survivor is to determine the feasibility of classifying (or differentiating) brain activity uniquely associated with wrist extension from that of wrist flexion, first in healthy adults. APPROACH We studied brain signal of 10 healthy adults, who performed wrist extension and wrist flexion during fMRI data acquisition. We selected four types of analyses to study the feasibility of differentiating brain signal driving wrist extension versus wrist flexion, as follows: 1) general linear model (GLM) analysis; 2) support vector machine (SVM) classification; 3) 'Winner Take All'; and 4) Relative Dominance. RESULTS With these four methods and our data, we found that few voxels were uniquely active during either wrist extension or wrist flexion. SVM resulted in only minimal classification accuracies. There was no significant difference in activation magnitude between wrist extension versus flexion; however, clusters of voxels showed extension signal > flexion signal and other clusters vice versa. Spatial patterns of activation differed among subjects. SIGNIFICANCE We encountered a number of obstacles to obtaining clear group results in healthy adults. These obstacles included the following: high variability across healthy adults in all measures studied; close proximity of uniquely active voxels to voxels that were common to both the extension and flexion movements; in general, higher magnitude of signal for the voxels common to both movements versus the magnitude of any given uniquely active voxel for one type of movement. Our results indicate that greater precision in imaging will be required to develop a truly effective method for differentiating wrist extension versus wrist flexion from fMRI data.
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Affiliation(s)
- Aniruddh Ravindran
- J. Pruitt Family Department of Biomedical Engineering, College of Engineering, University of Florida, Gainesville, Florida, United States of America
- Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, Florida, United States of America
| | - Jake D. Rieke
- Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, Florida, United States of America
| | - Jose Daniel Alcantara Zapata
- J. Pruitt Family Department of Biomedical Engineering, College of Engineering, University of Florida, Gainesville, Florida, United States of America
- Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, Florida, United States of America
| | - Keith D. White
- Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, Florida, United States of America
- Department of Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, Florida, United States of America
| | - Avi Matarasso
- Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, Florida, United States of America
- Department of Chemical Engineering, College of Engineering, University of Florida, Gainesville, Florida, United States of America
| | - M. Minhal Yusufali
- J. Pruitt Family Department of Biomedical Engineering, College of Engineering, University of Florida, Gainesville, Florida, United States of America
- Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, Florida, United States of America
| | - Mohit Rana
- Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Aysegul Gunduz
- J. Pruitt Family Department of Biomedical Engineering, College of Engineering, University of Florida, Gainesville, Florida, United States of America
| | - Mo Modarres
- Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, Florida, United States of America
| | - Ranganatha Sitaram
- Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Department of Psychiatry and Division of Neuroscience, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Janis J. Daly
- J. Pruitt Family Department of Biomedical Engineering, College of Engineering, University of Florida, Gainesville, Florida, United States of America
- Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, Florida, United States of America
- Department of Neurology, College of Medicine, University of Florida, Gainesville, United States of America
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Grigorev NA, Savosenkov AO, Lukoyanov MV, Udoratina A, Shusharina NN, Kaplan AY, Hramov AE, Kazantsev VB, Gordleeva S. A BCI-Based Vibrotactile Neurofeedback Training Improves Motor Cortical Excitability During Motor Imagery. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1583-1592. [PMID: 34343094 DOI: 10.1109/tnsre.2021.3102304] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
In this study, we address the issue of whether vibrotactile feedback can enhance the motor cortex excitability translated into the plastic changes in local cortical areas during motor imagery (MI) BCI-based training. For this purpose, we focused on two of the most notable neurophysiological effects of MI - the event-related desynchronization (ERD) level and the increase in cortical excitability assessed with navigated transcranial magnetic stimulation (nTMS). For TMS navigation, we used individual high-resolution 3D brain MRIs. Ten BCI-naive and healthy adults participated in this study. The MI (rest or left/right hand imagery using Graz-BCI paradigm) tasks were performed separately in the presence and absence of feedback. To investigate how much the presence/absence of vibrotactile feedback in MI BCI-based training could contribute to the sensorimotor cortical activations, we compared the MEPs amplitude during MI after training with and without feedback. In addition, the ERD levels during MI BCI-based training were investigated. Our findings provide evidence that applying vibrotactile feedback during MI training leads to (i) an enhancement of the desynchronization level of mu-rhythm EEG patterns over the contralateral motor cortex area corresponding to the MI of the non-dominant hand; (ii) an increase in motor cortical excitability in hand muscle representation corresponding to a muscle engaged by the MI.
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85
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Li S, Jin J, Daly I, Wang X, Lam HK, Cichocki A. Enhancing P300 based character recognition performance using a combination of ensemble classifiers and a fuzzy fusion method. J Neurosci Methods 2021; 362:109300. [PMID: 34343575 DOI: 10.1016/j.jneumeth.2021.109300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 07/14/2021] [Accepted: 07/29/2021] [Indexed: 11/17/2022]
Abstract
BACKGROUND P300-based brain-computer interfaces provide communication pathways without the need for muscle activity by recognizing electrical signals from the brain. The P300 speller is one of the most commonly used BCI applications, as it is very simple and reliable, and it is capable of reaching satisfactory communication performance. However, as with other BCIs, it remains a challenge to improve the P300 speller's performance to increase its practical usability. NEW METHODS In this study, we propose a novel multi-feature subset fuzzy fusion (MSFF) framework for the P300 speller to recognize the users' spelling intention. This method includes two parts: 1) feature selection by the Lasso algorithm and feature division; 2) the construction of ensemble LDA classifiers and the fuzzy fusion of those classifiers to recognize user intention. RESULTS The proposed framework is evaluated in three public datasets and achieves an average accuracy of 100% after 4 epochs for BCI Competition II Dataset IIb, 96% for BCI Competition III dataset II and 98.3% for the BNCI Horizon Dataset. It indicates that the proposed MSFF method can make use of temporal information of signals and helps to enhance classification performance. COMPARISON WITH EXISTING METHODS The proposed MSFF method yields better or comparable performance than previously reported machine learning algorithms. CONCLUSIONS The proposed MSFF method is able to improve the performance of P300-based BCIs.
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Affiliation(s)
- Shurui Li
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Jing Jin
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China.
| | - Ian Daly
- Brain-Computer Interfacing and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex CO4 3SQ, UK
| | - Xingyu Wang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Hak-Keung Lam
- Department of Engineering, King's College London, London WC2R 2LS, UK
| | - Andrzej Cichocki
- Skolkovo Institute of Science and Technology (SKOLTECH), 143026 Moscow, Russia; Systems Research Institute PAS, Warsaw, Poland; Nicolaus Copernicus University (UMK), Torun, Poland
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86
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Orlandi S, House SC, Karlsson P, Saab R, Chau T. Brain-Computer Interfaces for Children With Complex Communication Needs and Limited Mobility: A Systematic Review. Front Hum Neurosci 2021; 15:643294. [PMID: 34335203 PMCID: PMC8319030 DOI: 10.3389/fnhum.2021.643294] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 05/18/2021] [Indexed: 11/13/2022] Open
Abstract
Brain-computer interfaces (BCIs) represent a new frontier in the effort to maximize the ability of individuals with profound motor impairments to interact and communicate. While much literature points to BCIs' promise as an alternative access pathway, there have historically been few applications involving children and young adults with severe physical disabilities. As research is emerging in this sphere, this article aims to evaluate the current state of translating BCIs to the pediatric population. A systematic review was conducted using the Scopus, PubMed, and Ovid Medline databases. Studies of children and adolescents that reported BCI performance published in English in peer-reviewed journals between 2008 and May 2020 were included. Twelve publications were identified, providing strong evidence for continued research in pediatric BCIs. Research evidence was generally at multiple case study or exploratory study level, with modest sample sizes. Seven studies focused on BCIs for communication and five on mobility. Articles were categorized and grouped based on type of measurement (i.e., non-invasive and invasive), and the type of brain signal (i.e., sensory evoked potentials or movement-related potentials). Strengths and limitations of studies were identified and used to provide requirements for clinical translation of pediatric BCIs. This systematic review presents the state-of-the-art of pediatric BCIs focused on developing advanced technology to support children and youth with communication disabilities or limited manual ability. Despite a few research studies addressing the application of BCIs for communication and mobility in children, results are encouraging and future works should focus on customizable pediatric access technologies based on brain activity.
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Affiliation(s)
- Silvia Orlandi
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
| | - Sarah C. House
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
| | - Petra Karlsson
- Cerebral Palsy Alliance, The University of Sydney, Sydney, NSW, Australia
| | - Rami Saab
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
| | - Tom Chau
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
- Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON, Canada
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87
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A neuromimetic realization of hippocampal CA1 for theta wave generation. Neural Netw 2021; 142:548-563. [PMID: 34340189 DOI: 10.1016/j.neunet.2021.07.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 04/29/2021] [Accepted: 07/02/2021] [Indexed: 11/20/2022]
Abstract
Recent advances in neural engineering allowed the development of neuroprostheses which facilitate functionality in people with neurological problems. In this research, a real-time neuromorphic system is proposed to artificially reproduce the theta wave and firing patterns of different neuronal populations in the CA1, a sub-region of the hippocampus. The hippocampal theta oscillations (4-12 Hz) are an important electrophysiological rhythm that contributes in various cognitive functions, including navigation, memory, and novelty detection. The proposed CA1 neuromimetic circuit includes 100 linearized Pinsky-Rinzel neurons and 668 excitatory and inhibitory synapses on a field programmable gate array (FPGA). The implemented spiking neural network of the CA1 includes the main neuronal populations for the theta rhythm generation: excitatory pyramidal cells, PV+ basket cells, and Oriens Lacunosum-Moleculare (OLM) cells which are inhibitory interneurons. Moreover, the main inputs to the CA1 region from the entorhinal cortex via the perforant pathway, the CA3 via Schaffer collaterals, and the medial septum via fimbria-fornix are also implemented on the FPGA using a bursting leaky-integrate and fire (LIF) neuron model. The results of hardware realization show that the proposed CA1 neuromimetic circuit successfully reconstructs the theta oscillations and functionally illustrates the phase relations between firing responses of the different neuronal populations. It is also evaluated the impact of medial septum elimination on the firing patterns of the CA1 neuronal population and the theta wave's characteristics. This neuromorphic system can be considered as a potential platform that opens opportunities for neuroprosthetic applications in future works.
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88
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Wang Y, Luo J, Guo Y, Du Q, Cheng Q, Wang H. Changes in EEG Brain Connectivity Caused by Short-Term BCI Neurofeedback-Rehabilitation Training: A Case Study. Front Hum Neurosci 2021; 15:627100. [PMID: 34366808 PMCID: PMC8336868 DOI: 10.3389/fnhum.2021.627100] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Accepted: 05/31/2021] [Indexed: 12/22/2022] Open
Abstract
Background In combined with neurofeedback, Motor Imagery (MI) based Brain-Computer Interface (BCI) has been an effective long-term treatment therapy for motor dysfunction caused by neurological injury in the brain (e.g., post-stroke hemiplegia). However, individual neurological differences have led to variability in the single sessions of rehabilitation training. Research on the impact of short training sessions on brain functioning patterns can help evaluate and standardize the short duration of rehabilitation training. In this paper, we use the electroencephalogram (EEG) signals to explore the brain patterns’ changes after a short-term rehabilitation training. Materials and Methods Using an EEG-BCI system, we analyzed the changes in short-term (about 1-h) MI training data with and without visual feedback, respectively. We first examined the EEG signal’s Mu band power’s attenuation caused by Event-Related Desynchronization (ERD). Then we use the EEG’s Event-Related Potentials (ERP) features to construct brain networks and evaluate the training from multiple perspectives: small-scale based on single nodes, medium-scale based on hemispheres, and large-scale based on all-brain. Results Results showed no significant difference in the ERD power attenuation estimation in both groups. But the neurofeedback group’s ERP brain network parameters had substantial changes and trend properties compared to the group without feedback. The neurofeedback group’s Mu band power’s attenuation increased but not significantly (fitting line slope = 0.2, t-test value p > 0.05) after the short-term MI training, while the non-feedback group occurred an insignificant decrease (fitting line slope = −0.4, t-test value p > 0.05). In the ERP-based brain network analysis, the neurofeedback group’s network parameters were attenuated in all scales significantly (t-test value: p < 0.01); while the non-feedback group’s most network parameters didn’t change significantly (t-test value: p > 0.05). Conclusion The MI-BCI training’s short-term effects does not show up in the ERD analysis significantly but can be detected by ERP-based network analysis significantly. Results inspire the efficient evaluation of short-term rehabilitation training and provide a useful reference for subsequent studies.
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Affiliation(s)
- Youhao Wang
- Academy for Engineering and Technology, Fudan University (FAET), Shanghai, China
| | - Jingjing Luo
- Academy for Engineering and Technology, Fudan University (FAET), Shanghai, China.,Jihua Laboratory, Foshan, China
| | - Yuzhu Guo
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
| | - Qiang Du
- Academy for Engineering and Technology, Fudan University (FAET), Shanghai, China
| | - Qiying Cheng
- Academy for Engineering and Technology, Fudan University (FAET), Shanghai, China
| | - Hongbo Wang
- Academy for Engineering and Technology, Fudan University (FAET), Shanghai, China
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89
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Tao X, Yi W, Wang K, He F, Qi H. Inter-stimulus phase coherence in steady-state somatosensory evoked potentials and its application in improving the performance of single-channel MI-BCI. J Neural Eng 2021; 18. [PMID: 34077914 DOI: 10.1088/1741-2552/ac0767] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 06/02/2021] [Indexed: 11/12/2022]
Abstract
Objective. With the development of clinical applications of motor imagery-based brain-computer interfaces (MI-BCIs), a single-channel MI-BCI system that can be easily assembled is an attractive goal. However, due to the low quality of the spectral power features in the traditional MI-BCI paradigm, the recognition performance of current single-channel systems is far lower than that of multi-channel systems, impeding their use in clinical applications.Approach.In this study, the subjects' right and left hands were stimulated simultaneously at different frequencies to induce steady-state somatosensory evoked potentials (SSSEP). Subjects then performed motor imagery (MI) tasks. A new electroencephalography (EEG) index, inter-stimulus phase coherence (ISPC), was built to measure phase desynchronization of SSSEP caused by MI. Then, ISPC is introduced as a feature into left-hand and right-hand MI recognition.Main results.ISPC analysis found that left-handed MI can cause a significant decrease in phase synchronization in contralateral sensorimotor SSSEP, while right-handed MI has little effect on it, and vice versa. Combining ISPC features with traditional spectral power features, the single-channel left-hand versus right-hand MI recognition accuracy reaches 81.0%, which is much higher than that observed with traditional MI paradigms (about 60%).Significance.This work shows that the hybrid MI-SSSEP paradigm can provide more sensitive EEG features to decode motor intentions, demonstrating its potential for clinical applications.
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Affiliation(s)
- Xuewen Tao
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Weibo Yi
- Beijing Machine and Equipment Institute, Beijing, People's Republic of China
| | - Kun Wang
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Feng He
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China.,Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China
| | - Hongzhi Qi
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China.,Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China
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90
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Matarasso AK, Rieke JD, White K, Yusufali MM, Daly JJ. Combined real-time fMRI and real time fNIRS brain computer interface (BCI): Training of volitional wrist extension after stroke, a case series pilot study. PLoS One 2021; 16:e0250431. [PMID: 33956845 PMCID: PMC8101762 DOI: 10.1371/journal.pone.0250431] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 04/01/2021] [Indexed: 02/07/2023] Open
Abstract
OBJECTIVE Pilot testing of real time functional magnetic resonance imaging (rt-fMRI) and real time functional near infrared spectroscopy (rt-fNIRS) as brain computer interface (BCI) neural feedback systems combined with motor learning for motor recovery in chronic severely impaired stroke survivors. APPROACH We enrolled a four-case series and administered three sequential rt-fMRI and ten rt-fNIRS neural feedback sessions interleaved with motor learning sessions. Measures were: Arm Motor Assessment Tool, functional domain (AMAT-F; 13 complex functional tasks), Fugl-Meyer arm coordination scale (FM); active wrist extension range of motion (ROM); volume of activation (fMRI); and fNIRS HbO concentration. Performance during neural feedback was assessed, in part, using percent successful brain modulations during rt-fNIRS. MAIN RESULTS Pre-/post-treatment mean clinically significant improvement in AMAT-F (.49 ± 0.22) and FM (10.0 ± 3.3); active wrist ROM improvement ranged from 20° to 50°. Baseline to follow-up change in brain signal was as follows: fMRI volume of activation was reduced in almost all ROIs for three subjects, and for one subject there was an increase or no change; fNIRS HbO was within normal range, except for one subject who increased beyond normal at post-treatment. During rt-fNIRS neural feedback training, there was successful brain signal modulation (42%-78%). SIGNIFICANCE Severely impaired stroke survivors successfully engaged in spatially focused BCI systems, rt-fMRI and rt-fNIRS, to clinically significantly improve motor function. At the least, equivalency in motor recovery was demonstrated with prior long-duration motor learning studies (without neural feedback), indicating that no loss of motor improvement resulted from substituting neural feedback sessions for motor learning sessions. Given that the current neural feedback protocol did not prevent the motor improvements observed in other long duration studies, even in the presence of fewer sessions of motor learning in the current work, the results support further study of neural feedback and its potential for recovery of motor function in stroke survivors. In future work, expanding the sophistication of either or both rt-fMRI and rt-fNIRS could hold the potential for further reducing the number of hours of training needed and/or the degree of recovery. ClinicalTrials.gov ID: NCT02856035.
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Affiliation(s)
- Avi K. Matarasso
- Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, Florida, United States of America
- Department of Chemical Engineering, College of Engineering, University of Florida, Gainesville, Florida, United States of America
| | - Jake D. Rieke
- Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, Florida, United States of America
| | - Keith White
- Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, Florida, United States of America
| | - M. Minhal Yusufali
- Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, Florida, United States of America
- J. Pruitt Family Department of Biomedical Engineering, College of Engineering, University of Florida, Gainesville, Florida, United States of America
| | - Janis J. Daly
- Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, Florida, United States of America
- Department of Neurology, College of Medicine, University of Florida, Gainesville, Florida, United States of America
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91
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Cao L, Li G, Xu Y, Zhang H, Shu X, Zhang D. A brain-actuated robotic arm system using non-invasive hybrid brain-computer interface and shared control strategy. J Neural Eng 2021; 18. [PMID: 33862607 DOI: 10.1088/1741-2552/abf8cb] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 04/16/2021] [Indexed: 01/20/2023]
Abstract
Objective.The electroencephalography (EEG)-based brain-computer interfaces (BCIs) have been used in the control of robotic arms. The performance of non-invasive BCIs may not be satisfactory due to the poor quality of EEG signals, so the shared control strategies were tried as an alternative solution. However, most of the existing shared control methods set the arbitration rules manually, which highly depended on the specific tasks and developer's experience. In this study, we proposed a novel shared control model that automatically optimized the control commands in a dynamical way based on the context in real-time control. Besides, we employed the hybrid BCI to better allocate commands with multiple functions. The system allowed non-invasive BCI users to manipulate a robotic arm moving in a three-dimensional (3D) space and complete a pick-place task of multiple objects.Approach.Taking the scene information obtained by computer vision as a knowledge base, a machine agent was designed to infer the user's intention and generate automatic commands. Based on the inference confidence and user's characteristic, the proposed shared control model fused the machine autonomy and human intention dynamically for robotic arm motion optimization during the online control. In addition, we introduced a hybrid BCI scheme that applied steady-state visual evoked potentials and motor imagery to the divided primary and secondary BCI interfaces to better allocate the BCI resources (e.g. decoding computing power, screen occupation) and realize the multi-dimensional control of the robotic arm.Main results.Eleven subjects participated in the online experiments of picking and placing five objects that scattered at different positions in a 3D workspace. The results showed that most of the subjects could control the robotic arm to complete accurate and robust picking task with an average success rate of approximately 85% under the shared control strategy, while the average success rate of placing task controlled by pure BCI was 50% approximately.Significance.In this paper, we proposed a novel shared controller for motion automatic optimization, together with a hybrid BCI control scheme that allocated paradigms according to the importance of commands to realize multi-dimensional and effective control of a robotic arm. Our study indicated that the shared control strategy with hybrid BCI could greatly improve the performance of the brain-actuated robotic arm system.
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Affiliation(s)
- Linfeng Cao
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Guangye Li
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Yang Xu
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Heng Zhang
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Xiaokang Shu
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Dingguo Zhang
- Department of Electronic and Electrical Engineering, University of Bath, Bath, United Kingdom
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Rathee D, Raza H, Roy S, Prasad G. A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface. Sci Data 2021; 8:120. [PMID: 33927204 PMCID: PMC8085139 DOI: 10.1038/s41597-021-00899-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 03/22/2021] [Indexed: 11/09/2022] Open
Abstract
Recent advancements in magnetoencephalography (MEG)-based brain-computer interfaces (BCIs) have shown great potential. However, the performance of current MEG-BCI systems is still inadequate and one of the main reasons for this is the unavailability of open-source MEG-BCI datasets. MEG systems are expensive and hence MEG datasets are not readily available for researchers to develop effective and efficient BCI-related signal processing algorithms. In this work, we release a 306-channel MEG-BCI data recorded at 1KHz sampling frequency during four mental imagery tasks (i.e. hand imagery, feet imagery, subtraction imagery, and word generation imagery). The dataset contains two sessions of MEG recordings performed on separate days from 17 healthy participants using a typical BCI imagery paradigm. The current dataset will be the only publicly available MEG imagery BCI dataset as per our knowledge. The dataset can be used by the scientific community towards the development of novel pattern recognition machine learning methods to detect brain activities related to motor imagery and cognitive imagery tasks using MEG signals.
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Affiliation(s)
- Dheeraj Rathee
- School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO4 3SQ, United Kingdom
| | - Haider Raza
- School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO4 3SQ, United Kingdom.
| | - Sujit Roy
- School of Computing, Engineering & Intelligent System, Ulster University, Derry~Londonderry, BT48 7JL, United Kingdom
| | - Girijesh Prasad
- School of Computing, Engineering & Intelligent System, Ulster University, Derry~Londonderry, BT48 7JL, United Kingdom
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93
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Ayodele KP, Akinboboye EA, Komolafe MA. The performance of a low-cost bio-amplifier on 3D human arm movement reconstruction. ACTA ACUST UNITED AC 2021; 65:577-585. [PMID: 32463379 DOI: 10.1515/bmt-2019-0085] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Accepted: 01/31/2020] [Indexed: 11/15/2022]
Abstract
Objectives In this study, the performance of OpenBCI, a low-cost bio-amplifier, is assessed when used for 3D motion reconstruction. Methods Eleven scalp electrode locations from three subjects were used, with sampling rate of 125 Hz, subsequently band-pass filtered from 0.5 to 40 Hz. After segmentation into epochs, information-rich frequency ranges were determined using filter bank common spatial filter. Simultaneously, the actual hand motions of subjects were captured using a Microsoft Kinect sensor. Multimodal data streams were synchronized using the lab streaming layer (LSL) application. A modified version of an existing multiple linear regression models was employed to learn the relationship between the electroencephalography (EEG) feature input and the recorded kinematic data. To assess system performance with limited data, 10-fold cross validation was used. Results The most information-rich frequency bands for subjects were found to be in the ranges of 5 - 9 Hz and 33 - 37 Hz. Hand lateralization accuracy for the three subjects were 97.4, 78.7 and 96.9% respectively. 3D position reconstructed with an average correlation coefficient of 0.21, 0.47 and 0.38 respectively along three pre-defined axes, with the corresponding average correlation coefficients for velocity being 0.21, 0.36 and 0.25 respectively. The results compare favourably with a cross-section of existing results, while cost-per-electrode costs were 76% lower than the average per-electrode cost for similar systems and 44% lower than the cheapest previously-reported system. Conclusions This study has shown that low-cost bio-amplifiers such as the OpenBCI can be used for 3D motion reconstruction tasks.
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Affiliation(s)
- Kayode P Ayodele
- Department of Electronic and Electrical Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
| | - Eniola A Akinboboye
- Department of Electronic and Electrical Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
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94
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Influential Factors of an Asynchronous BCI for Movement Intention Detection. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2020:8573754. [PMID: 32273902 PMCID: PMC7125445 DOI: 10.1155/2020/8573754] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 02/02/2020] [Accepted: 02/10/2020] [Indexed: 11/17/2022]
Abstract
In recent years, asynchronous brain computer interface (BCI) systems have been utilized in many domains such as robot controlling, assistive technology, and rehabilitation. In such BCI systems, movement intention detection algorithms are used to detect movement desires. In recent years, movement-related cortical potential (MRCP), an electroencephalogram (EEG) pattern representing voluntary movement intention, attracts wide attention in movement intention detection. Unfortunately, low MRCP detection accuracy makes the asynchronous BCI system impractical for real usage. In order to develop an effective MRCP detection algorithm, EEG data have to be properly preprocessed. In this work, we investigate the relationship and effects of three factors including frequency bands, spatial filters, and classifiers on MRCP classification performance to determine best settings. In particular, we performed a systematic performance investigation on combinations of five frequency bands, five spatial filters, and six classifiers. The EEG data were acquired from subjects performing series of self-paced ankle dorsiflexions. Analysis of variance (ANOVA) statistical test was performed on F1 scores to investigate effects of these three factors. The results show that frequency bands and spatial filters depend on each other. The combinations directly affect the F1 scores, so they have to be chosen carefully. The results can be used as guidelines for BCI researchers to effectively design a preprocessing method for an advanced asynchronous BCI system, which can assist the stroke rehabilitation.
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95
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Toledo F, Thaler M. Gamma frequencies as a predictor for the accomplishment of a motor task guided through the action observation network. NeuroRehabilitation 2021; 48:139-148. [PMID: 33386819 DOI: 10.3233/nre-201508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Action observation describes a concept where the subsequent motor behavior of an individual can be modulated though observing an action. This occurs through the activation of neurons in the action observation network, acting on a variety of motor learning processes. This network has been proven highly useful in the rehabilitation of patients with acquired brain injury, placing "action observation" as one of the most effective techniques for motor recovery in physical neurorehabilitation. OBJECTIVE The aim of this paper is to define an EEG marker for motor learning, guided through observation. METHODS Healthy subjects (n = 41) participated voluntarily for this research. They were asked to repeat an unknown motor behavior, immediately after observing a video. During the observation, EEG raw signals where collected with a portable EEG and the results were later compared with success and fail on repeating the motor procedure. The comparison was then analyzed with the Mann-Whitney U test for non-parametrical data, with a confidence interval of 95%. RESULTS A significant relation between motor performance and neural activity was found for Alpha (p = 0,0149) and Gamma (0,0005) oscillatory patterns. CONCLUSION Gamma oscillations with frequencies between 41 and 49,75 Hz, seem to be an adequate EEG marker for motor performance guided through the action observation network. The technology used for this paper is easy to use, low-cost and presents valid measurements for the recommended oscillatory frequencies, implying a possible use on rehabilitation, by collecting data in real-time during therapeutic interventions and assessments.
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Affiliation(s)
- Felippe Toledo
- Lunex International University of Health, Exercise and Sports, Differdange, Luxembourg
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96
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Tuckute G, Hansen ST, Kjaer TW, Hansen LK. Real-Time Decoding of Attentional States Using Closed-Loop EEG Neurofeedback. Neural Comput 2021; 33:967-1004. [PMID: 33513324 DOI: 10.1162/neco_a_01363] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 10/16/2020] [Indexed: 11/04/2022]
Abstract
Sustained attention is a cognitive ability to maintain task focus over extended periods of time (Mackworth, 1948; Chun, Golomb, & Turk-Browne, 2011). In this study, scalp electroencephalography (EEG) signals were processed in real time using a 32 dry-electrode system during a sustained visual attention task. An attention training paradigm was implemented, as designed in DeBettencourt, Cohen, Lee, Norman, and Turk-Browne (2015) in which the composition of a sequence of blended images is updated based on the participant's decoded attentional level to a primed image category. It was hypothesized that a single neurofeedback training session would improve sustained attention abilities. Twenty-two participants were trained on a single neurofeedback session with behavioral pretraining and posttraining sessions within three consecutive days. Half of the participants functioned as controls in a double-blinded design and received sham neurofeedback. During the neurofeedback session, attentional states to primed categories were decoded in real time and used to provide a continuous feedback signal customized to each participant in a closed-loop approach. We report a mean classifier decoding error rate of 34.3% (chance = 50%). Within the neurofeedback group, there was a greater level of task-relevant attentional information decoded in the participant's brain before making a correct behavioral response than before an incorrect response. This effect was not visible in the control group (interaction p=7.23e-4), which strongly indicates that we were able to achieve a meaningful measure of subjective attentional state in real time and control participants' behavior during the neurofeedback session. We do not provide conclusive evidence whether the single neurofeedback session per se provided lasting effects in sustained attention abilities. We developed a portable EEG neurofeedback system capable of decoding attentional states and predicting behavioral choices in the attention task at hand. The neurofeedback code framework is Python based and open source, and it allows users to actively engage in the development of neurofeedback tools for scientific and translational use.
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Affiliation(s)
- Greta Tuckute
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark, and Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, 02139, U.S.A.,
| | - Sofie Therese Hansen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark,
| | - Troels Wesenberg Kjaer
- Department of Neurology, Zealand University Hospital, 4000 Roskilde, Denmark, and Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark,
| | - Lars Kai Hansen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark,
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97
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Jeong H, Kim J. Development of a Guidance System for Motor Imagery Enhancement Using the Virtual Hand Illusion. SENSORS 2021; 21:s21062197. [PMID: 33801070 PMCID: PMC8003913 DOI: 10.3390/s21062197] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 03/09/2021] [Accepted: 03/18/2021] [Indexed: 01/09/2023]
Abstract
Motor imagery (MI) is widely used to produce input signals for brain-computer interfaces (BCI) due to the similarities between MI-BCI and the planning-execution cycle. Despite its usefulness, MI tasks can be ambiguous to users and MI produces weaker cortical signals than motor execution. Existing MI guidance systems, which have been reported to provide visual guidance for MI and enhance MI, still have limitations: insufficient immersion for MI or poor expandability to MI for another body parts. We propose a guidance system for MI enhancement that can immerse users in MI and will be easy to extend to other body parts and target motions with few physical constraints. To make easily extendable MI guidance system, the virtual hand illusion is applied to the MI guidance system with a motion tracking sensor. MI enhancement was evaluated in 11 healthy people by comparison with another guidance system and conventional motor commands for BCI. The results showed that the proposed MI guidance system produced an amplified cortical signal compared to pure MI (p < 0.017), and a similar cortical signal as those produced by both actual execution (p > 0.534) and an MI guidance system with the rubber hand illusion (p > 0.722) in the contralateral region. Therefore, we believe that the proposed MI guidance system with the virtual hand illusion is a viable alternative to existing MI guidance systems in various applications with MI-BCI.
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98
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Jovanovic LI, Kapadia N, Zivanovic V, Rademeyer HJ, Alavinia M, McGillivray C, Kalsi-Ryan S, Popovic MR, Marquez-Chin C. Brain-computer interface-triggered functional electrical stimulation therapy for rehabilitation of reaching and grasping after spinal cord injury: a feasibility study. Spinal Cord Ser Cases 2021; 7:24. [PMID: 33741900 PMCID: PMC7979732 DOI: 10.1038/s41394-020-00380-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 12/21/2020] [Accepted: 12/25/2020] [Indexed: 11/08/2022] Open
Abstract
STUDY DESIGN Feasibility and preliminary clinical efficacy analysis in a single-arm interventional study. OBJECTIVES We developed a brain-computer interface-triggered functional electrical stimulation therapy (BCI-FEST) system for clinical application and conducted an interventional study to (1) assess its feasibility and (2) understand its potential clinical efficacy for the rehabilitation of reaching and grasping in individuals with sub-acute spinal cord injury (SCI). SETTING Spinal cord injury rehabilitation hospital-Toronto Rehabilitation Institute-Lyndhurst Centre. METHODS Five participants with sub-acute SCI completed between 12 and 40 1-hour sessions using BCI-FEST, with up to 5 sessions a week. We assessed feasibility by measuring participants' compliance with treatment, the occurrence of adverse events, BCI sensitivity, and BCI setup duration. Clinical efficacy was assessed using Functional Independence Measure (FIM) and Spinal Cord Independence Measure (SCIM), as primary outcomes. In addition, we used two upper-limb function tests as secondary outcomes. RESULTS On average, participants completed 29.8 sessions with no adverse events. Only one of the 149 sessions was affected by technical challenges. The BCI sensitivity ranged between 69.5 and 80.2%, and the mean BCI setup duration was ~11 min. In the primary outcomes, three out of five participants showed changes greater than the minimal clinically important differences (MCIDs). Additionally, the mean change in secondary outcome measures met the threshold for detecting MCID as well; four out of five participants achieved MCID. CONCLUSIONS The new BCI-FEST intervention is safe, feasible, and promising for the rehabilitation of reaching and grasping after SCI.
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Affiliation(s)
- Lazar I Jovanovic
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.
- The KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.
- CRANIA, University Health Network, Toronto, ON, Canada.
| | - Naaz Kapadia
- The KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada
- CRANIA, University Health Network, Toronto, ON, Canada
- Rehabilitation Sciences Institute, University of Toronto, Toronto, ON, Canada
| | - Vera Zivanovic
- The KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada
| | - Hope Jervis Rademeyer
- The KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada
- Rehabilitation Sciences Institute, University of Toronto, Toronto, ON, Canada
| | - Mohammad Alavinia
- The KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada
| | - Colleen McGillivray
- Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada
- Department of Medicine, Division of Physical Medicine and Rehabilitation, University of Toronto, Toronto, ON, Canada
| | - Sukhvinder Kalsi-Ryan
- The KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada
- Rehabilitation Sciences Institute, University of Toronto, Toronto, ON, Canada
| | - Milos R Popovic
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- The KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada
- CRANIA, University Health Network, Toronto, ON, Canada
- Rehabilitation Sciences Institute, University of Toronto, Toronto, ON, Canada
| | - Cesar Marquez-Chin
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- The KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada
- CRANIA, University Health Network, Toronto, ON, Canada
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99
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Neo PSH, Mayne T, Fu X, Huang Z, Franz EA. Crosstalk disrupts the production of motor imagery brain signals in brain-computer interfaces. Health Inf Sci Syst 2021; 9:13. [PMID: 33786162 DOI: 10.1007/s13755-021-00142-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 02/24/2021] [Indexed: 10/21/2022] Open
Abstract
Brain-computer interfaces (BCIs) target specific brain activity for neuropsychological rehabilitation, and also allow patients with motor disabilities to control mobility and communication devices. Motor imagery of single-handed actions is used in BCIs but many users cannot control the BCIs effectively, limiting applications in the health systems. Crosstalk is unintended brain activations that interfere with bimanual actions and could also occur during motor imagery. To test if crosstalk impaired BCI user performance, we recorded EEG in 46 participants while they imagined movements in four experimental conditions using motor imagery: left hand (L), right hand (R), tongue (T) and feet (F). Pairwise classification accuracies of the tasks were compared (LR, LF, LT, RF, RT, FT), using common spatio-spectral filters and linear discriminant analysis. We hypothesized that LR classification accuracy would be lower than every other combination that included a hand imagery due to crosstalk. As predicted, classification accuracy for LR (58%) was reliably the lowest. Interestingly, participants who showed poor LR classification also demonstrated at least one good TR, TL, FR or FL classification; and good LR classification was detected in 16% of the participants. For the first time, we showed that crosstalk occurred in motor imagery, and affected BCI performance negatively. Such effects are effector-sensitive regardless of the BCI methods used; and likely not apparent to the user or the BCI developer. This means that tasks choice is crucial when designing BCI. Critically, the effects of crosstalk appear mitigatable. We conclude that understanding crosstalk mitigation is important for improving BCI applicability. Supplementary Information The online version of this article contains supplementary material available (10.1007/s13755-021-00142-y).
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Affiliation(s)
- Phoebe S-H Neo
- Department of Computer Science, University of Otago, Dunedin, New Zealand.,Department of Psychology, University of Otago, Dunedin, New Zealand
| | - Terence Mayne
- Department of Computer Science, University of Otago, Dunedin, New Zealand.,Department of Psychology, University of Otago, Dunedin, New Zealand
| | - Xiping Fu
- Department of Computer Science, University of Otago, Dunedin, New Zealand
| | - Zhiyi Huang
- Department of Computer Science, University of Otago, Dunedin, New Zealand
| | - Elizabeth A Franz
- Department of Psychology, University of Otago, Dunedin, New Zealand.,fMRI Otago, Dunedin, New Zealand
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100
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Ding Q, Lin T, Wu M, Yang W, Li W, Jing Y, Ren X, Gong Y, Xu G, Lan Y. Influence of iTBS on the Acute Neuroplastic Change After BCI Training. Front Cell Neurosci 2021; 15:653487. [PMID: 33776653 PMCID: PMC7994768 DOI: 10.3389/fncel.2021.653487] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 02/22/2021] [Indexed: 12/21/2022] Open
Abstract
Objective: Brain-computer interface (BCI) training is becoming increasingly popular in neurorehabilitation. However, around one third subjects have difficulties in controlling BCI devices effectively, which limits the application of BCI training. Furthermore, the effectiveness of BCI training is not satisfactory in stroke rehabilitation. Intermittent theta burst stimulation (iTBS) is a powerful neural modulatory approach with strong facilitatory effects. Here, we investigated whether iTBS would improve BCI accuracy and boost the neuroplastic changes induced by BCI training. Methods: Eight right-handed healthy subjects (four males, age: 20-24) participated in this two-session study (BCI-only session and iTBS+BCI session in random order). Neuroplastic changes were measured by functional near-infrared spectroscopy (fNIRS) and single-pulse transcranial magnetic stimulation (TMS). In BCI-only session, fNIRS was measured at baseline and immediately after BCI training. In iTBS+BCI session, BCI training was followed by iTBS delivered on the right primary motor cortex (M1). Single-pulse TMS was measured at baseline and immediately after iTBS. fNIRS was measured at baseline, immediately after iTBS, and immediately after BCI training. Paired-sample t-tests were used to compare amplitudes of motor-evoked potentials, cortical silent period duration, oxygenated hemoglobin (HbO2) concentration and functional connectivity across time points, and BCI accuracy between sessions. Results: No significant difference in BCI accuracy was detected between sessions (p > 0.05). In BCI-only session, functional connectivity matrices between motor cortex and prefrontal cortex were significantly increased after BCI training (p's < 0.05). In iTBS+BCI session, amplitudes of motor-evoked potentials were significantly increased after iTBS (p's < 0.05), but no change in HbO2 concentration or functional connectivity was observed throughout the whole session (p's > 0.05). Conclusions: To our knowledge, this is the first study that investigated how iTBS targeted on M1 influences BCI accuracy and the acute neuroplastic changes after BCI training. Our results revealed that iTBS targeted on M1 did not influence BCI accuracy or facilitate the neuroplastic changes after BCI training. Therefore, M1 might not be an effective stimulation target of iTBS for the purpose of improving BCI accuracy or facilitate its effectiveness; other brain regions (i.e., prefrontal cortex) are needed to be further investigated as potentially effective stimulation targets.
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Affiliation(s)
- Qian Ding
- Department of Rehabilitation Medicine, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Tuo Lin
- Department of Rehabilitation Medicine, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Manfeng Wu
- Department of Rehabilitation Medicine, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Wenqing Yang
- Department of Rehabilitation Medicine, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Wanqi Li
- Department of Rehabilitation Medicine, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Yinghua Jing
- Department of Rehabilitation Medicine, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Xiaoqing Ren
- Department of Rehabilitation Medicine, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Yulai Gong
- Sichuan Provincial Rehabilitation Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Guangqing Xu
- Department of Rehabilitation Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yue Lan
- Department of Rehabilitation Medicine, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
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