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Kim JS, Kim H, Chung CK, Kim JS. Dual model transfer learning to compensate for individual variability in brain-computer interface. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 254:108294. [PMID: 38943984 DOI: 10.1016/j.cmpb.2024.108294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Revised: 06/14/2024] [Accepted: 06/16/2024] [Indexed: 07/01/2024]
Abstract
BACKGROUND AND OBJECTIVE Recent advancements in brain-computer interface (BCI) technology have seen a significant shift towards incorporating complex decoding models such as deep neural networks (DNNs) to enhance performance. These models are particularly crucial for sophisticated tasks such as regression for decoding arbitrary movements. However, these BCI models trained and tested on individual data often face challenges with limited performance and generalizability across different subjects. This limitation is primarily due to a tremendous number of parameters of DNN models. Training complex models demands extensive datasets. Nevertheless, group data from many subjects may not produce sufficient decoding performance because of inherent variability in neural signals both across individuals and over time METHODS: To address these challenges, this study proposed a transfer learning approach that could effectively adapt to subject-specific variability in cortical regions. Our method involved training two separate movement decoding models: one on individual data and another on pooled group data. We then created a salience map for each cortical region from the individual model, which helped us identify the input's contribution variance across subjects. Based on the contribution variance, we combined individual and group models using a modified knowledge distillation framework. This approach allowed the group model to be universally applicable by assigning greater weights to input data, while the individual model was fine-tuned to focus on areas with significant individual variance RESULTS: Our combined model effectively encapsulated individual variability. We validated this approach with nine subjects performing arm-reaching tasks, with our method outperforming (mean correlation coefficient, r = 0.75) both individual (r = 0.70) and group models (r = 0.40) in decoding performance. In particular, there were notable improvements in cases where individual models showed low performances (e.g., r = 0.50 in the individual decoder to r = 0.61 in the proposed decoder) CONCLUSIONS: These results not only demonstrate the potential of our method for robust BCI, but also underscore its ability to generalize individual data for broader applicability.
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Affiliation(s)
- Jun Su Kim
- Dept. of Brain and Cognitive Sciences, Seoul National University, Seoul, Republic of Korea; Clinical Research Institute, Konkuk University Medical Center Seoul, Republic of Korea
| | - HongJune Kim
- Clinical Research Institute, Konkuk University Medical Center Seoul, Republic of Korea
| | - Chun Kee Chung
- Dept. of Brain and Cognitive Sciences, Seoul National University, Seoul, Republic of Korea; Interdisciplinary Program in Neuroscience, Seoul National University, Seoul, Republic of Korea; Dept. of Neurosurgery, Seoul National University Hospital, Seoul, Republic of Korea; Neuroscience Research Institute, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - June Sic Kim
- Clinical Research Institute, Konkuk University Medical Center Seoul, Republic of Korea; Research Institute of Biomedical Science & Technology, Konkuk University, Seoul, Republic of Korea.
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Richer N, Bradford JC, Ferris DP. Mobile neuroimaging: What we have learned about the neural control of human walking, with an emphasis on EEG-based research. Neurosci Biobehav Rev 2024; 162:105718. [PMID: 38744350 DOI: 10.1016/j.neubiorev.2024.105718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 04/18/2024] [Accepted: 05/08/2024] [Indexed: 05/16/2024]
Abstract
Our understanding of the neural control of human walking has changed significantly over the last twenty years and mobile brain imaging methods have contributed substantially to current knowledge. High-density electroencephalography (EEG) has the advantages of being lightweight and mobile while providing temporal resolution of brain changes within a gait cycle. Advances in EEG hardware and processing methods have led to a proliferation of research on the neural control of locomotion in neurologically intact adults. We provide a narrative review of the advantages and disadvantages of different mobile brain imaging methods, then summarize findings from mobile EEG studies quantifying electrocortical activity during human walking. Contrary to historical views on the neural control of locomotion, recent studies highlight the widespread involvement of many areas, such as the anterior cingulate, posterior parietal, prefrontal, premotor, sensorimotor, supplementary motor, and occipital cortices, that show active fluctuations in electrical power during walking. The electrocortical activity changes with speed, stability, perturbations, and gait adaptation. We end with a discussion on the next steps in mobile EEG research.
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Affiliation(s)
- Natalie Richer
- Department of Kinesiology and Applied Health, University of Winnipeg, Winnipeg, Manitoba, Canada.
| | - J Cortney Bradford
- US Army Combat Capabilities Development Command US Army Research Laboratory, Adelphi, MD, USA
| | - Daniel P Ferris
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA
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Tharawadeepimuk K, Limroongreungrat W, Pilanthananond M, Nanbancha A. Auditory Cue Effects on Gait-Phase-Dependent Electroencephalogram (EEG) Modulations during Overground and Treadmill Walking. SENSORS (BASEL, SWITZERLAND) 2024; 24:1548. [PMID: 38475084 DOI: 10.3390/s24051548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 02/02/2024] [Accepted: 02/21/2024] [Indexed: 03/14/2024]
Abstract
Walking rehabilitation following injury or disease involves voluntary gait modification, yet the specific brain signals underlying this process remains unclear. This aim of this study was to investigate the impact of an auditory cue on changes in brain activity when walking overground (O) and on a treadmill (T) using an electroencephalogram (EEG) with a 32-electrode montage. Employing a between-group repeated-measures design, 24 participants (age: 25.7 ± 3.8 years) were randomly allocated to either an O (n = 12) or T (n = 12) group to complete two walking conditions (self-selected speed control (sSC) and speed control (SC)). The differences in brain activities during the gait cycle were investigated using statistical non-parametric mapping (SnPM). The addition of an auditory cue did not modify cortical activity in any brain area during the gait cycle when walking overground (all p > 0.05). However, significant differences in EEG activity were observed in the delta frequency band (0.5-4 Hz) within the sSC condition between the O and T groups. These differences occurred at the central frontal (loading phase) and frontocentral (mid stance phase) brain areas (p < 0.05). Our data suggest auditory cueing has little impact on modifying cortical activity during overground walking. This may have practical implications in neuroprosthesis development for walking rehabilitation, sports performance optimization, and overall human quality-of-life improvement.
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Affiliation(s)
| | | | | | - Ampika Nanbancha
- College of Sports Science and Technology, Mahidol University, Nakhon Pathom 73170, Thailand
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Khajuria A, Sharma R, Joshi D. EEG Dynamics of Locomotion and Balancing: Solution to Neuro-Rehabilitation. Clin EEG Neurosci 2024; 55:143-163. [PMID: 36052404 DOI: 10.1177/15500594221123690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The past decade has witnessed tremendous growth in analyzing the cortical representation of human locomotion and balance using Electroencephalography (EEG). With the advanced developments in miniaturized electronics, wireless brain recording systems have been developed for mobile recordings, such as in locomotion. In this review, the cortical dynamics during locomotion are presented with extensive focus on motor imagery, and employing the treadmill as a tool for performing different locomotion tasks. Further, the studies that examine the cortical dynamics during balancing, focusing on two types of balancing tasks, ie, static and dynamic, with the challenges in sensory inputs and cognition (dual-task), are presented. Moreover, the current literature demonstrates the advancements in signal processing methods to detect and remove the artifacts from EEG signals. Prior studies show the electrocortical sources in the anterior cingulate, posterior parietal, and sensorimotor cortex was found to be activated during locomotion. The event-related potential has been observed to increase in the fronto-central region for a wide range of balance tasks. The advanced knowledge of cortical dynamics during mobility can benefit various application areas such as neuroprosthetics and gait/balance rehabilitation. This review will be beneficial for the development of neuroprostheses, and rehabilitation devices for patients suffering from movement or neurological disorders.
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Affiliation(s)
- Aayushi Khajuria
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Richa Sharma
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Deepak Joshi
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
- Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India
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Amrani El Yaakoubi N, McDonald C, Lennon O. Prediction of Gait Kinematics and Kinetics: A Systematic Review of EMG and EEG Signal Use and Their Contribution to Prediction Accuracy. Bioengineering (Basel) 2023; 10:1162. [PMID: 37892892 PMCID: PMC10604078 DOI: 10.3390/bioengineering10101162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 09/29/2023] [Accepted: 09/30/2023] [Indexed: 10/29/2023] Open
Abstract
Human-machine interfaces hold promise in enhancing rehabilitation by predicting and responding to subjects' movement intent. In gait rehabilitation, neural network architectures utilize lower-limb muscle and brain activity to predict continuous kinematics and kinetics during stepping and walking. This systematic review, spanning five databases, assessed 16 papers meeting inclusion criteria. Studies predicted lower-limb kinematics and kinetics using electroencephalograms (EEGs), electromyograms (EMGs), or a combination with kinematic data and anthropological parameters. Long short-term memory (LSTM) and convolutional neural network (CNN) tools demonstrated highest accuracies. EEG focused on joint angles, while EMG predicted moments and torque joints. Useful EEG electrode locations included C3, C4, Cz, P3, F4, and F8. Vastus Lateralis, Rectus Femoris, and Gastrocnemius were the most commonly accessed muscles for kinematic and kinetic prediction using EMGs. No studies combining EEGs and EMGs to predict lower-limb kinematics and kinetics during stepping or walking were found, suggesting a potential avenue for future development in this technology.
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Affiliation(s)
| | | | - Olive Lennon
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, D04 V1W8 Dublin, Ireland; (N.A.E.Y.)
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Starkweather CK, Morrison MA, Yaroshinsky M, Louie K, Balakid J, Presbrey K, Starr PA, Wang DD. Human upper extremity motor cortex activity shows distinct oscillatory signatures for stereotyped arm and leg movements. Front Hum Neurosci 2023; 17:1212963. [PMID: 37635808 PMCID: PMC10449648 DOI: 10.3389/fnhum.2023.1212963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 07/25/2023] [Indexed: 08/29/2023] Open
Abstract
Introduction Stepping and arm swing are stereotyped movements that require coordination across multiple muscle groups. It is not known whether the encoding of these stereotyped movements in the human primary motor cortex is confined to the limbs' respective somatotopy. Methods We recorded subdural electrocorticography activities from the hand/arm area in the primary motor cortex of 6 subjects undergoing deep brain stimulation surgery for essential tremor and Parkinson's disease who performed stepping (all patients) and arm swing (n = 3 patients) tasks. Results We show stepping-related low frequency oscillations over the arm area. Furthermore, we show that this oscillatory activity is separable, both in frequency and spatial domains, from gamma band activity changes that occur during arm swing. Discussion Our study contributes to the growing body of evidence that lower extremity movement may be more broadly represented in the motor cortex, and suggest that it may represent a way to coordinate stereotyped movements across the upper and lower extremities.
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Affiliation(s)
- Clara Kwon Starkweather
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Melanie A. Morrison
- Department of Radiology, University of California, San Francisco, San Francisco, CA, United States
| | - Maria Yaroshinsky
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Kenneth Louie
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Jannine Balakid
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Kara Presbrey
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Philip A. Starr
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Doris D. Wang
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
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Fu X, Guan C. Gait Pattern Recognition Based on Supervised Contrastive Learning Between EEG and EMG. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082819 DOI: 10.1109/embc40787.2023.10340491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Electroencephalography (EEG) and lower-limb electromyography (EMG) signals are widely used in lower-limb kinematic classification and regression tasks. Since it directly measures muscle responses, EMG usually works better. However, due to the susceptibility of EMG signals to muscle fatigue, insufficient residual myoelectric activity, and the difficulty of precise localization, it is difficult to acquire EMG signals in practice. In contrast, EEG signals are stable and easy to sample. Therefore, in this work, we propose a multimodal training strategy based on supervised contrastive learning. With this training strategy, we can effectively use the guiding role of EMG in the training phase to help the model fit the gait with EEG signal while using only EEG signal in the testing phase to obtain better results than using any single modal signal to train and test the model. Finally, we compared the models trained with the strategy proposed in this paper with other models trained with EEG signals. The obtained Pearson's Correlation Coefficient value exceeds those of all baseline models.
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Fernandez-Del-Olmo M, Sánchez-Molina JA, Novo-Ponte S, Fogelson N. Directed connectivity in Parkinson's disease patients during over-ground and treadmill walking. Exp Gerontol 2023; 178:112220. [PMID: 37230335 DOI: 10.1016/j.exger.2023.112220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 05/17/2023] [Accepted: 05/22/2023] [Indexed: 05/27/2023]
Abstract
Treadmill walking is considered a useful therapeutic tool for improving gait in Parkinson's disease (PD) patients. The study investigated the role of top-down, frontal-parietal versus bottom-up parietal-frontal networks, during over-ground and treadmill walking in PD and control subjects, using functional connectivity. To this end, EEG was recorded simultaneously, during a ten-minute period of continuous walking either over-ground or on a treadmill, in thirteen PD patients and thirteen age-matched controls. We evaluated EEG directed connectivity, using phase transfer entropy in three frequency bands: theta, alpha and beta. PD patients showed increased top-down connectivity during over-ground compared with treadmill walking, in the beta frequency range. Control subjects showed no significant differences in connectivity between the two walking conditions. Our results suggest that in PD patients, OG walking was associated with increased allocation of attentional resources, compared with that on the TL. These functional connectivity modulations may shed further light on the mechanisms underlying treadmill versus overground walking in PD.
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Affiliation(s)
| | | | - Sabela Novo-Ponte
- Department of Neurology, Hospital Universitario Puerta de Hierro, Majadahonda, Madrid, Spain
| | - Noa Fogelson
- Department of Humanities, University Rey Juan Carlos, Madrid, Spain.
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Wang P, Cao X, Zhou Y, Gong P, Yousefnezhad M, Shao W, Zhang D. A comprehensive review on motion trajectory reconstruction for EEG-based brain-computer interface. Front Neurosci 2023; 17:1086472. [PMID: 37332859 PMCID: PMC10272365 DOI: 10.3389/fnins.2023.1086472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 05/03/2023] [Indexed: 06/20/2023] Open
Abstract
The advance in neuroscience and computer technology over the past decades have made brain-computer interface (BCI) a most promising area of neurorehabilitation and neurophysiology research. Limb motion decoding has gradually become a hot topic in the field of BCI. Decoding neural activity related to limb movement trajectory is considered to be of great help to the development of assistive and rehabilitation strategies for motor-impaired users. Although a variety of decoding methods have been proposed for limb trajectory reconstruction, there does not yet exist a review that covers the performance evaluation of these decoding methods. To alleviate this vacancy, in this paper, we evaluate EEG-based limb trajectory decoding methods regarding their advantages and disadvantages from a variety of perspectives. Specifically, we first introduce the differences in motor execution and motor imagery in limb trajectory reconstruction with different spaces (2D and 3D). Then, we discuss the limb motion trajectory reconstruction methods including experiment paradigm, EEG pre-processing, feature extraction and selection, decoding methods, and result evaluation. Finally, we expound on the open problem and future outlooks.
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Affiliation(s)
| | | | | | | | | | - Wei Shao
- *Correspondence: Wei Shao, ; Daoqiang Zhang,
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10
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Naro A, Pignolo L, Bruschetta D, Calabrò RS. Data on a novel approach examining the role of the cerebellum in gait performance improvement in patients with Parkinson disease receiving neurologic music therapy. Data Brief 2023; 47:109013. [PMID: 36936642 PMCID: PMC10014267 DOI: 10.1016/j.dib.2023.109013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 01/24/2023] [Accepted: 02/16/2023] [Indexed: 03/02/2023] Open
Abstract
Individuals with idiopathic Parkinson's disease (PD) benefit from Rhythmic Auditory Stimulation (RAS) concerning gait impairment recovery. In PD, RAS may help eliciting rhythmic and automatized motor responses, including gait, by bypassing the deteriorated internal "clock" within basal ganglia for automatic and rhythmic motricity. We aimed at exploring the contribution of the cerebellum to this "bypass effect" in response to RAS. To this end, we examined the cerebellum-cerebral connectivity indices using conventional EEG recording to assess whether the cerebellum contributes to RAS-based post-training effects in persons with PD. Fifty PD patients were randomly assigned to an 8-week training program using Gait-Trainer3 with or without RAS. We measured the Functional Gait Assessment, the Unified Parkinson's Disease Rating Scale, the Berg Balance Scale, the Tinetti Falls Efficacy Scale, the 10-meter walking test, the timed up-and-go test, and the gait quality index derived from gait analysis before and after the end of the training. A standard EEG during gait on the GT3 was also recorded and submitted to eLORETA analysis. Particularly, we focused on the time course of the gait-related activities (which were characterized using the maximum amplitude vertex across the gait cycles) within each brain region of interest. These clinical and electrophysiological measures could be used to monitor the improvement in gait performance in standard clinical settings and to develop new rehabilitation protocols focusing on a holistic functional recovery approach.
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Affiliation(s)
- Antonino Naro
- IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy
| | | | | | - Rocco Salvatore Calabrò
- IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy
- Corresponding author at: IRCCS Centro Neurolesi Bonino Pulejo; via Palermo, SS113, C.da Casazza, 98124 Messina, Italy.
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He X, Lei L, Yu G, Lin X, Sun Q, Chen S. Asymmetric cortical activation in healthy and hemiplegic individuals during walking: A functional near-infrared spectroscopy neuroimaging study. Front Neurol 2023; 13:1044982. [PMID: 36761919 PMCID: PMC9905619 DOI: 10.3389/fneur.2022.1044982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 12/22/2022] [Indexed: 01/26/2023] Open
Abstract
Background This study investigated the cortical activation mechanism underlying locomotor control during healthy and hemiplegic walking. Methods A total of eight healthy individuals with right leg dominance (male patients, 75%; mean age, 40.06 ± 4.53 years) and six post-stroke patients with right hemiplegia (male patients, 86%; mean age, 44.41 ± 7.23 years; disease course, 5.21 ± 2.63 months) completed a walking task at a treadmill speed of 2 km/h and a functional electrical stimulation (FES)-assisted walking task, respectively. Functional near-infrared spectroscopy (fNIRS) was used to detect hemodynamic changes in neuronal activity in the bilateral sensorimotor cortex (SMC), supplementary motor area (SMA), and premotor cortex (PMC). Results fNIRS cortical mapping showed more SMC-PMC-SMA locomotor network activation during hemiplegic walking than during healthy gait. Furthermore, more SMA and PMC activation in the affected hemisphere was observed during the FES-assisted hemiplegic walking task than during the non-FES-assisted task. The laterality index indicated asymmetric cortical activation during hemiplegic gait, with relatively greater activation in the unaffected (right) hemisphere during hemiplegic gait than during healthy walking. During hemiplegic walking, the SMC and SMA were predominantly activated in the unaffected hemisphere, whereas the PMC was predominantly activated in the affected hemisphere. No significant differences in the laterality index were noted between the other groups and regions (p > 0.05). Conclusion An important feature of asymmetric cortical activation was found in patients with post-stroke during the walking process, which was the recruitment of more SMC-SMA-PMC activation than in healthy individuals. Interestingly, there was no significant lateralized activation during hemiplegic walking with FES assistance, which would seem to indicate that FES may help hemiplegic walking recover the balance in cortical activation. These results, which are worth verifying through additional research, suggest that FES used as a potential therapeutic strategy may play an important role in motor recovery after stroke.
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Affiliation(s)
- Xiaokuo He
- Department of Rehabilitative Medicine, Fifth Hospital of Xiamen, Xiamen, China
| | - Lei Lei
- Department of Rehabilitative Medicine, Fifth Hospital of Xiamen, Xiamen, China
| | - Guo Yu
- Department of Rehabilitative Medicine, Fifth Hospital of Xiamen, Xiamen, China
| | - Xin Lin
- Department of Rehabilitative Medicine, Fifth Hospital of Xiamen, Xiamen, China
| | - Qianqian Sun
- Department of Rehabilitative Medicine, Xiangyang Central Hospital, Xiangyang, Hubei, China,Qianqian Sun ✉
| | - Shanjia Chen
- Department of Rehabilitative Medicine, Fifth Hospital of Xiamen, Xiamen, China,Department of Rehabilitative Medicine, The First Affiliated Hospital of Xiamen University, Xiamen, China,*Correspondence: Shanjia Chen ✉
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Korivand S, Jalili N, Gong J. Experiment protocols for brain-body imaging of locomotion: A systematic review. Front Neurosci 2023; 17:1051500. [PMID: 36937690 PMCID: PMC10014824 DOI: 10.3389/fnins.2023.1051500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 02/06/2023] [Indexed: 03/05/2023] Open
Abstract
Introduction Human locomotion is affected by several factors, such as growth and aging, health conditions, and physical activity levels for maintaining overall health and well-being. Notably, impaired locomotion is a prevalent cause of disability, significantly impacting the quality of life of individuals. The uniqueness and high prevalence of human locomotion have led to a surge of research to develop experimental protocols for studying the brain substrates, muscle responses, and motion signatures associated with locomotion. However, from a technical perspective, reproducing locomotion experiments has been challenging due to the lack of standardized protocols and benchmarking tools, which impairs the evaluation of research quality and the validation of previous findings. Methods This paper addresses the challenges by conducting a systematic review of existing neuroimaging studies on human locomotion, focusing on the settings of experimental protocols, such as locomotion intensity, duration, distance, adopted brain imaging technologies, and corresponding brain activation patterns. Also, this study provides practical recommendations for future experiment protocols. Results The findings indicate that EEG is the preferred neuroimaging sensor for detecting brain activity patterns, compared to fMRI, fNIRS, and PET. Walking is the most studied human locomotion task, likely due to its fundamental nature and status as a reference task. In contrast, running has received little attention in research. Additionally, cycling on an ergometer at a speed of 60 rpm using fNIRS has provided some research basis. Dual-task walking tasks are typically used to observe changes in cognitive function. Moreover, research on locomotion has primarily focused on healthy individuals, as this is the scenario most closely resembling free-living activity in real-world environments. Discussion Finally, the paper outlines the standards and recommendations for setting up future experiment protocols based on the review findings. It discusses the impact of neurological and musculoskeletal factors, as well as the cognitive and locomotive demands, on the experiment design. It also considers the limitations imposed by the sensing techniques used, including the acceptable level of motion artifacts in brain-body imaging experiments and the effects of spatial and temporal resolutions on brain sensor performance. Additionally, various experiment protocol constraints that need to be addressed and analyzed are explained.
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Affiliation(s)
- Soroush Korivand
- Department of Mechanical Engineering, The University of Alabama, Tuscaloosa, AL, United States
- Department of Computer Science, The University of Alabama, Tuscaloosa, AL, United States
| | - Nader Jalili
- Department of Mechanical Engineering, The University of Alabama, Tuscaloosa, AL, United States
| | - Jiaqi Gong
- Department of Computer Science, The University of Alabama, Tuscaloosa, AL, United States
- *Correspondence: Jiaqi Gong
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Ao M, Ren S, Yu Y, Huang H, Miao X, Ao Y, Wang W. The effects of blurred visual inputs with different levels on the cerebral activity during free level walking. Front Neurosci 2023; 17:1151799. [PMID: 37139527 PMCID: PMC10149992 DOI: 10.3389/fnins.2023.1151799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 03/20/2023] [Indexed: 05/05/2023] Open
Abstract
Objective The aim of this study was to evaluate the effects of blurred vision on electrocortical activities at different levels during walking. Materials and methods A total of 22 healthy volunteers (all men; mean age: 24.4 ± 3.9 years) underwent an electroencephalography (EEG) test synchronous with free level walking. Visual status was simulated by goggles covered by the occlusion foil targeted at a Snellen visual acuity of 20/60 (V0.3), 20/200 (V0.1), and light perception (V0). At each of these conditions, the participants completed barefoot walking for five blocks of 10 m. The EEG signals were recorded by a wireless EEG system with electrodes of interest, namely, Cz, Pz, Oz, O1, and O2. The gait performances were assessed by the Vicon system. Results During walking with normal vision (V1.0), there were cerebral activities related to visual processing, characterized as higher spectral power of delta (Oz and O2 vs. Cz, Pz, and O1, p ≤ 0.033) and theta (Oz vs. Cz and O1, p = 0.044) bands in occipital regions. Moderately blurred vision (V0.3) would attenuate the predominance of delta- and theta-band activities at Oz and O2, respectively. At the statuses of V0.1 and V0, the higher power of delta (at V0.1 and V0, Oz, and O2 vs. Cz, Pz, and O1, p ≤ 0.047) and theta bands (at V0.1, Oz vs. Cz, p = 0.010; at V0, Oz vs. Cz, Pz, and O1, p ≤ 0.016) emerged again. The cautious gait pattern, characterized by a decrease in gait speed (p < 0.001), a greater amplitude of deviation from the right ahead (p < 0.001), a prolonged stance time (p = 0.001), a restricted range of motion in the hip on the right side (p ≤ 0.010), and an increased knee flexion during stance on the left side (p = 0.014), was only detected at the status of V0. The power of the alpha band at the status of V0 was higher than that at V1.0, V0.3, and V0.1 (p ≤ 0.011). Conclusion Mildly blurred visual inputs would elicit generalization of low-frequency band activity during walking. In circumstance to no effective visual input, locomotor navigation would rely on cerebral activity related to visual working memory. The threshold to trigger the shift might be the visual status that is as blurred as the level of Snellen visual acuity of 20/200.
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Affiliation(s)
- Mingxin Ao
- Department of Ophthalmology, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Peking University Third Hospital, Beijing, China
| | - Shuang Ren
- Department of Sports Medicine, Institute of Sports Medicine of Peking University, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Sports Injuries, Peking University Third Hospital, Beijing, China
| | - Yuanyuan Yu
- Department of Sports Medicine, Institute of Sports Medicine of Peking University, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Sports Injuries, Peking University Third Hospital, Beijing, China
| | - Hongshi Huang
- Department of Sports Medicine, Institute of Sports Medicine of Peking University, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Sports Injuries, Peking University Third Hospital, Beijing, China
| | - Xin Miao
- Department of Sports Medicine, Institute of Sports Medicine of Peking University, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Sports Injuries, Peking University Third Hospital, Beijing, China
| | - Yingfang Ao
- Department of Sports Medicine, Institute of Sports Medicine of Peking University, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Sports Injuries, Peking University Third Hospital, Beijing, China
- *Correspondence: Yingfang Ao
| | - Wei Wang
- Department of Ophthalmology, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Peking University Third Hospital, Beijing, China
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14
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Peterson SM, Rao RPN, Brunton BW. Learning neural decoders without labels using multiple data streams. J Neural Eng 2022; 19. [PMID: 35905727 DOI: 10.1088/1741-2552/ac857c] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 07/29/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Recent advances in neural decoding have accelerated the development of brain-computer interfaces aimed at assisting users with everyday tasks such as speaking, walking, and manipulating objects. However, current approaches for training neural decoders commonly require large quantities of labeled data, which can be laborious or infeasible to obtain in real-world settings. Alternatively, self-supervised models that share self-generated pseudo-labels between two data streams have shown exceptional performance on unlabeled audio and video data, but it remains unclear how well they extend to neural decoding. APPROACH We learn neural decoders without labels by leveraging multiple simultaneously recorded data streams, including neural, kinematic, and physiological signals. Specifically, we apply cross-modal, self-supervised deep clustering to train decoders that can classify movements from brain recordings. After training, we then isolate the decoders for each input data stream and compare the accuracy of decoders trained using cross-modal deep clustering against supervised and unimodal, self-supervised models. MAIN RESULTS We find that sharing pseudo-labels between two data streams during training substantially increases decoding performance compared to unimodal, self-supervised models, with accuracies approaching those of supervised decoders trained on labeled data. Next, we extend cross-modal decoder training to three or more modalities, achieving state-of-the-art neural decoding accuracy that matches or slightly exceeds the performance of supervised models. Significance: We demonstrate that cross-modal, self-supervised decoding can be applied to train neural decoders when few or no labels are available and extend the cross-modal framework to share information among three or more data streams, further improving self-supervised training.
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Affiliation(s)
- Steven M Peterson
- Biology, University of Washington, 4000 15th Ave NE, Seattle, Washington, 98195, UNITED STATES
| | - Rajesh P N Rao
- Department of Computer Science and Engineering College of Engineering, University of Washington, Box 352350, Seattle, Washington, 98195, UNITED STATES
| | - Bingni W Brunton
- University of Washington, 4000 15th Ave NE, Seattle, Washington, 98195, UNITED STATES
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15
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High-accuracy wearable detection of freezing of gait in Parkinson's disease based on pseudo-multimodal features. Comput Biol Med 2022; 146:105629. [DOI: 10.1016/j.compbiomed.2022.105629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 03/25/2022] [Accepted: 04/15/2022] [Indexed: 11/22/2022]
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16
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Shin J, Yang S, Park C, Lee Y, You SJH. Comparative effects of passive and active mode robot-assisted gait training on brain and muscular activities in sub-acute and chronic stroke. NeuroRehabilitation 2022; 51:51-63. [PMID: 35311717 DOI: 10.3233/nre-210304] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Robot-assisted gait training (RAGT) was initially developed based on the passive controlled (PC) mode, where the target or ideal locomotor kinematic trajectory is predefined and a patient basically 'rides' the robot instead of actively participating in the actual locomotor relearning process. A new insightful contemporary neuroscience and mechatronic evidence suggest that robotic-based locomotor relearning can be best achieved through active interactive (AI) mode rather than PC mode. OBJECTIVE The purpose of this study was to compare the pattern of gait-related cortical activity, specifically gait event-related spectral perturbations (ERSPs), and muscle activity from the tibialis anterior (TA) and clinical functional tests in subacute and chronic stroke patients during robot-assisted gait training (RAGT) in passive controlled (PC) and active interactive (AI) modes. METHODS The present study involves a two-group pretest-posttest design in which two groups (i.e., PC-RAGT group and AI-RAGT group) of 14 stroke subjects were measured to assess changes in ERSPs, the muscle activation of TA, and the clinical functional tests, following 15- 18 sessions of intervention according to the protocol of each group. RESULTS Our preliminary results demonstrated that the power in the μ band (8- 12 Hz) was increased in the leg area of sensorimotor cortex (SMC) and supplementary motor area (SMA) at post-intervention as compared to pre-intervention in both groups. Such cortical neuroplasticity change was associated with TA muscle activity during gait and functional independence in functional ambulation category (FAC) and motor coordination in Fugl- Meyer Assessment for lower extremity (FMA-LE) test as well as spasticity in the modified Ashworth scale (MAS) measures. CONCLUSIONS We have first developed a novel neuroimaging experimental paradigm which distinguished gait event related cortical involvement between pre- and post-intervention with PC-RAGT and AI-RAGT in individuals with subacute and chronic hemiparetic stroke.
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Affiliation(s)
- Jiwon Shin
- Sports Movement Artificial-Intelligence Robotics Technology (SMART) Institute, Department of Physical Therapy, Yonsei University, Wonju, Republic of Korea.,Department of Physical Therapy, Yonsei University, Wonju, Republic of Korea
| | - Sejung Yang
- Department of Biomedical Engineering, Yonsei University, Wonju, Republic of Korea
| | - Chanhee Park
- Sports Movement Artificial-Intelligence Robotics Technology (SMART) Institute, Department of Physical Therapy, Yonsei University, Wonju, Republic of Korea.,Department of Physical Therapy, Yonsei University, Wonju, Republic of Korea
| | - Yongseok Lee
- Myongji-Choonhey Rehabilitation Hospital, Seoul, Republic of Korea
| | - Sung Joshua H You
- Sports Movement Artificial-Intelligence Robotics Technology (SMART) Institute, Department of Physical Therapy, Yonsei University, Wonju, Republic of Korea.,Department of Physical Therapy, Yonsei University, Wonju, Republic of Korea
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17
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Pakniyat N, Namazi H. Complexity-Based Analysis of the Variations of Brain and Muscle Reactions in Walking and Standing Balance While Receiving Different Perturbations. Front Hum Neurosci 2021; 15:749082. [PMID: 34690727 PMCID: PMC8531105 DOI: 10.3389/fnhum.2021.749082] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 09/06/2021] [Indexed: 11/18/2022] Open
Abstract
In this article, we evaluated the variations of the brain and muscle activations while subjects are exposed to different perturbations to walking and standing balance. Since EEG and EMG signals have complex structures, we utilized the complexity-based analysis. Specifically, we analyzed the fractal dimension and sample entropy of Electroencephalogram (EEG) and Electromyogram (EMG) signals while subjects walked and stood, and received different perturbations in the form of pulling and rotation (via virtual reality). The results showed that the complexity of EEG signals was higher in walking than standing as the result of different perturbations. However, the complexity of EMG signals was higher in standing than walking as the result of different perturbations. Therefore, the alterations in the complexity of EEG and EMG signals are inversely correlated. This analysis could be extended to investigate simultaneous variations of rhythmic patterns of other physiological signals while subjects perform different activities.
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Affiliation(s)
| | - Hamidreza Namazi
- Incubator of Kinanthropology Research, Faculty of Sports Studies, Masaryk University, Brno, Czechia.,College of Engineering and Science, Victoria University, Melbourne, VIC, Australia
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18
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Silvernagel MP, Ling AS, Nuyujukian P. A markerless platform for ambulatory systems neuroscience. Sci Robot 2021; 6:eabj7045. [PMID: 34516749 DOI: 10.1126/scirobotics.abj7045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Motor systems neuroscience seeks to understand how the brain controls movement. To minimize confounding variables, large-animal studies typically constrain body movement from areas not under observation, ensuring consistent, repeatable behaviors. Such studies have fueled decades of research, but they may be artificially limiting the richness of neural data observed, preventing generalization to more natural movements and settings. Neuroscience studies of unconstrained movement would capture a greater range of behavior and a more complete view of neuronal activity, but instrumenting an experimental rig suitable for large animals presents substantial engineering challenges. Here, we present a markerless, full-body motion tracking and synchronized wireless neural electrophysiology platform for large, ambulatory animals. Composed of four depth (RGB-D) cameras that provide a 360° view of a 4.5-square-meters enclosed area, this system is designed to record a diverse range of neuroethologically relevant behaviors. This platform also allows for the simultaneous acquisition of hundreds of wireless neural recording channels in multiple brain regions. As behavioral and neuronal data are generated at rates below 200 megabytes per second, a single desktop can facilitate hours of continuous recording. This setup is designed for systems neuroscience and neuroengineering research, where synchronized kinematic behavior and neural data are the foundation for investigation. By enabling the study of previously unexplored movement tasks, this system can generate insights into the functioning of the mammalian motor system and provide a platform to develop brain-machine interfaces for unconstrained applications.
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Affiliation(s)
| | - Alissa S Ling
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Paul Nuyujukian
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.,Department of Bioengineering, Stanford University, Stanford, CA, USA.,Department of Neurosurgery, Stanford University, Stanford, CA, USA.,Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.,Stanford Bio-X, Stanford University, Stanford, CA, USA
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19
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Yokoyama H, Kaneko N, Watanabe K, Nakazawa K. Neural decoding of gait phases during motor imagery and improvement of the decoding accuracy by concurrent action observation. J Neural Eng 2021; 18. [PMID: 34082405 DOI: 10.1088/1741-2552/ac07bd] [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: 03/08/2021] [Accepted: 06/03/2021] [Indexed: 12/20/2022]
Abstract
Objective. Brain decoding of motor imagery (MI) not only is crucial for the control of neuroprosthesis but also provides insights into the underlying neural mechanisms. Walking consists of stance and swing phases, which are associated with different biomechanical and neural control features. However, previous knowledge on decoding the MI of gait is limited to simple information (e.g. the classification of 'walking' and 'rest').Approach. Here, we investigated the feasibility of electroencephalogram (EEG) decoding of the two gait phases during the MI of walking and whether the combined use of MI and action observation (AO) would improve decoding accuracy.Main results. We demonstrated that the stance and swing phases could be decoded from EEGs during MI or AO alone. We also demonstrated the decoding accuracy during MI was improved by concurrent AO. The decoding models indicated that the improved decoding accuracy following the combined use of MI and AO was facilitated by the additional information resulting from the concurrent cortical activations related to sensorimotor, visual, and action understanding systems associated with MI and AO.Significance. This study is the first to show that decoding the stance versus swing phases during MI is feasible. The current findings provide fundamental knowledge for neuroprosthetic design and gait rehabilitation, and they expand our understanding of the neural activity underlying AO, MI, and AO + MI of walking.Novelty and significanceBrain decoding of detailed gait-related information during motor imagery (MI) is important for brain-computer interfaces (BCIs) for gait rehabilitation. This study is the first to show the feasibility of EEG decoding of the stance versus swing phases during MI. We also demonstrated that the combined use of MI and action observation (AO) improves decoding accuracy, which is facilitated by the concurrent and synergistic involvement of the cortical activations for MI and AO. These findings extend the current understanding of neural activity and the combined effects of AO and MI and provide a basis for effective techniques for walking rehabilitation.
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Affiliation(s)
- Hikaru Yokoyama
- Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan.,Japan Society for the Promotion of Science, Tokyo 102-0083, Japan.,Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo 153-8902, Japan
| | - Naotsugu Kaneko
- Japan Society for the Promotion of Science, Tokyo 102-0083, Japan.,Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo 153-8902, Japan
| | - Katsumi Watanabe
- Faculty of Science and Engineering, Waseda University, Tokyo 169-8555, Japan.,Faculty of Arts, Design, and Architecture, University of New South Wales, Sydney, NSW 2021, Australia
| | - Kimitaka Nakazawa
- Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo 153-8902, Japan
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20
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Mercado L, Quiroz-Compean G, Azorín JM. Analyzing the performance of segmented trajectory reconstruction of lower limb movements from EEG signals with combinations of electrodes, gaps, and delays. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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21
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Delaux A, de Saint Aubert JB, Ramanoël S, Bécu M, Gehrke L, Klug M, Chavarriaga R, Sahel JA, Gramann K, Arleo A. Mobile brain/body imaging of landmark-based navigation with high-density EEG. Eur J Neurosci 2021; 54:8256-8282. [PMID: 33738880 PMCID: PMC9291975 DOI: 10.1111/ejn.15190] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 03/05/2021] [Accepted: 03/14/2021] [Indexed: 01/07/2023]
Abstract
Coupling behavioral measures and brain imaging in naturalistic, ecological conditions is key to comprehend the neural bases of spatial navigation. This highly integrative function encompasses sensorimotor, cognitive, and executive processes that jointly mediate active exploration and spatial learning. However, most neuroimaging approaches in humans are based on static, motion‐constrained paradigms and they do not account for all these processes, in particular multisensory integration. Following the Mobile Brain/Body Imaging approach, we aimed to explore the cortical correlates of landmark‐based navigation in actively behaving young adults, solving a Y‐maze task in immersive virtual reality. EEG analysis identified a set of brain areas matching state‐of‐the‐art brain imaging literature of landmark‐based navigation. Spatial behavior in mobile conditions additionally involved sensorimotor areas related to motor execution and proprioception usually overlooked in static fMRI paradigms. Expectedly, we located a cortical source in or near the posterior cingulate, in line with the engagement of the retrosplenial complex in spatial reorientation. Consistent with its role in visuo‐spatial processing and coding, we observed an alpha‐power desynchronization while participants gathered visual information. We also hypothesized behavior‐dependent modulations of the cortical signal during navigation. Despite finding few differences between the encoding and retrieval phases of the task, we identified transient time–frequency patterns attributed, for instance, to attentional demand, as reflected in the alpha/gamma range, or memory workload in the delta/theta range. We confirmed that combining mobile high‐density EEG and biometric measures can help unravel the brain structures and the neural modulations subtending ecological landmark‐based navigation.
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Affiliation(s)
- Alexandre Delaux
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
| | | | - Stephen Ramanoël
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
| | - Marcia Bécu
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
| | - Lukas Gehrke
- Institute of Psychology and Ergonomics, Technische Universität Berlin, Berlin, Germany
| | - Marius Klug
- Institute of Psychology and Ergonomics, Technische Universität Berlin, Berlin, Germany
| | - Ricardo Chavarriaga
- Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Zurich University of Applied Sciences, ZHAW Datalab, Winterthur, Switzerland
| | - José-Alain Sahel
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France.,CHNO des Quinze-Vingts, INSERM-DGOS CIC 1423, Paris, France.,Fondation Ophtalmologique Rothschild, Paris, France.,Department of Ophthalmology, The University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Klaus Gramann
- Institute of Psychology and Ergonomics, Technische Universität Berlin, Berlin, Germany
| | - Angelo Arleo
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
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22
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Alchalabi B, Faubert J, Labbé D. A multi-modal modified feedback self-paced BCI to control the gait of an avatar. J Neural Eng 2021; 18. [PMID: 33711832 DOI: 10.1088/1741-2552/abee51] [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: 07/27/2020] [Accepted: 03/12/2021] [Indexed: 11/12/2022]
Abstract
Brain-computer interfaces (BCI) have been used to control the gait of a virtual self-avatar with a proposed application in the field of gait rehabilitation. OBJECTIVE to develop a high performance multi-modal BCI to control single steps and forward walking of an immersive virtual reality avatar. This system will overcome the limitation of existing systems. APPROACH This system used MI of these actions, in cue-paced and self-paced modes. Twenty healthy participants participated in this 4 sessions study across 4 different days. They were cued to imagine a single step forward with their right or left foot, or to imagine walking forward. They were instructed to reach a target by using the MI of multiple steps (self-paced switch-control mode) or by maintaining MI of forward walking (continuous-control mode). The movement of the avatar was controlled by two calibrated RLDA classifiers that used the µ power spectral density (PSD) over the foot area of the motor cortex as a feature. The classifiers were retrained after every session. For a subset of the trials, positive modified feedback was presented to half of the participants. MAIN RESULTS All participants were able to operate the BCI. Their average offline performance, after retraining the classifiers was 86.0 ± 6.1%, showing that the recalibration of the classifiers enhanced the offline performance of the BCI (p < 0.01). The average online performance was 85.9 ± 8.4% showing that modified feedback enhanced BCI performance (p =0.001). The average performance was 83% at self-paced switch control and 92% at continuous control mode. SIGNIFICANCE This study reports on the first novel integration of different design approaches, different control modes and different performance enhancement techniques, all in parallel in one single high performance and multi-modal BCI system, to control single steps and forward walking of an immersive virtual reality avatar.
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Affiliation(s)
- Bilal Alchalabi
- biomedical engineering, University of Montreal, 2900 Boulevard Edouard mon Petit, Montreal, Quebec, H3C 3J7, CANADA
| | - Jocelyn Faubert
- Université de Montréal, 3744 Rue Jean Brillant, Montreal, Quebec, H3T 1P1, CANADA
| | - David Labbé
- École de technologie supérieure, 1100 Rue Notre-Dame ouest, Montreal, Quebec, H3C 1K3, CANADA
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23
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Mustile M, Kourtis D, Ladouce S, Learmonth G, Edwards MG, Donaldson DI, Ietswaart M. Mobile EEG reveals functionally dissociable dynamic processes supporting real-world ambulatory obstacle avoidance: Evidence for early proactive control. Eur J Neurosci 2021; 54:8106-8119. [PMID: 33465827 DOI: 10.1111/ejn.15120] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 12/07/2020] [Accepted: 12/28/2020] [Indexed: 11/29/2022]
Abstract
The ability to safely negotiate the world on foot takes humans years to develop, reflecting the extensive cognitive demands associated with real-time planning and control of walking. Despite the importance of walking, methodological limitations mean that surprisingly little is known about the neural and cognitive processes that support ambulatory motor control. Here, we report mobile EEG data recorded from 32 healthy young adults during real-world ambulatory obstacle avoidance. Participants walked along a path while stepping over expected and unexpected obstacles projected on the floor, allowing us to capture the dynamic oscillatory response to changes in environmental demands. Compared to obstacle-free walking, time-frequency analysis of the EEG data revealed clear neural markers of proactive and reactive forms of movement control (occurring before and after crossing an obstacle), visible as increases in frontal theta and centro-parietal beta power respectively. Critically, the temporal profile of changes in frontal theta allowed us to arbitrate between early selection and late adaptation mechanisms of proactive control. Our data show that motor plans are updated as soon as an upcoming obstacle appears, rather than when the obstacle is reached. In addition, regardless of whether motor plans required updating, a clear beta rebound was present after obstacles were crossed, reflecting the resetting of the motor system. Overall, mobile EEG recorded during real-world walking provides novel insight into the cognitive and neural basis of dynamic motor control in humans, suggesting new routes to the monitoring and rehabilitation of motor disorders such as dyspraxia and Parkinson's disease.
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Affiliation(s)
- Magda Mustile
- Psychology, Faculty of Natural Sciences, University of Stirling, Stirling, UK
| | - Dimitrios Kourtis
- Psychology, Faculty of Natural Sciences, University of Stirling, Stirling, UK
| | - Simon Ladouce
- Institut Supérieur de l'Aéronautique et de l'Espace (ISAE), Toulouse, France
| | - Gemma Learmonth
- Institute of Neuroscience & Psychology, University of Glasgow, Glasgow, UK
| | - Martin G Edwards
- Institute of Research in the Psychological Sciences, Université catholique de Louvain, Louvain-la-Neuve, Belgium
| | - David I Donaldson
- School of Psychology and Neuroscience, University of St Andrews, St. Andrews, UK
| | - Magdalena Ietswaart
- Psychology, Faculty of Natural Sciences, University of Stirling, Stirling, UK
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24
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Bhardwaj S, Khan AA, Muzammil M. Lower limb rehabilitation robotics: The current understanding and technology. Work 2021; 69:775-793. [PMID: 34180443 DOI: 10.3233/wor-205012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND With the increasing rate of ambulatory disabilities and rise in the elderly population, advance methods to deliver the rehabilitation and assistive services to patients have become important. Lower limb robotic therapeutic and assistive aids have been found to improve the rehabilitation outcome. OBJECTIVE The article aims to present the updated understanding in the field of lower limb rehabilitation robotics and identify future research avenues. METHODS Groups of keywords relating to assistive technology, rehabilitation robotics, and lower limb were combined and searched in EMBASE, IEEE Xplore Digital Library, Scopus, Web of Science and Google Scholar database. RESULTS Based on the literature collected from the databases we provide an overview of the understanding of robotics in rehabilitation and state of the art devices for lower limb rehabilitation. Technological advancements in rehabilitation robotic architecture (sensing, actuation and control) and biomechanical considerations in design have been discussed. Finally, a discussion on the major advances, research directions, and challenges is presented. CONCLUSIONS Although the use of robotics has shown a promising approach to rehabilitation and reducing the burden on caregivers, extensive and innovative research is still required in both cognitive and physical human-robot interaction to achieve treatment efficacy and efficiency.
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Affiliation(s)
- Siddharth Bhardwaj
- Department of Mechanical Engineering, Aligarh Muslim University, Aligarh, UP, India
| | - Abid Ali Khan
- Department of Mechanical Engineering, Aligarh Muslim University, Aligarh, UP, India
| | - Mohammad Muzammil
- Department of Mechanical Engineering, Aligarh Muslim University, Aligarh, UP, India
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25
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Tortora S, Tonin L, Chisari C, Micera S, Menegatti E, Artoni F. Hybrid Human-Machine Interface for Gait Decoding Through Bayesian Fusion of EEG and EMG Classifiers. Front Neurorobot 2020; 14:582728. [PMID: 33281593 PMCID: PMC7705173 DOI: 10.3389/fnbot.2020.582728] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 09/30/2020] [Indexed: 01/25/2023] Open
Abstract
Despite the advances in the field of brain computer interfaces (BCI), the use of the sole electroencephalography (EEG) signal to control walking rehabilitation devices is currently not viable in clinical settings, due to its unreliability. Hybrid interfaces (hHMIs) represent a very recent solution to enhance the performance of single-signal approaches. These are classification approaches that combine multiple human-machine interfaces, normally including at least one BCI with other biosignals, such as the electromyography (EMG). However, their use for the decoding of gait activity is still limited. In this work, we propose and evaluate a hybrid human-machine interface (hHMI) to decode walking phases of both legs from the Bayesian fusion of EEG and EMG signals. The proposed hHMI significantly outperforms its single-signal counterparts, by providing high and stable performance even when the reliability of the muscular activity is compromised temporarily (e.g., fatigue) or permanently (e.g., weakness). Indeed, the hybrid approach shows a smooth degradation of classification performance after temporary EMG alteration, with more than 75% of accuracy at 30% of EMG amplitude, with respect to the EMG classifier whose performance decreases below 60% of accuracy. Moreover, the fusion of EEG and EMG information helps keeping a stable recognition rate of each gait phase of more than 80% independently on the permanent level of EMG degradation. From our study and findings from the literature, we suggest that the use of hybrid interfaces may be the key to enhance the usability of technologies restoring or assisting the locomotion on a wider population of patients in clinical applications and outside the laboratory environment.
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Affiliation(s)
- Stefano Tortora
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Luca Tonin
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Carmelo Chisari
- Unit of Neurorehabilitation, Department of Medical Specialties, University Hospital of Pisa, Pisa, Italy
| | - Silvestro Micera
- Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna, The Biorobotics Institute, Pisa, Italy.,Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics and Institute of Bioengineering, Lausanne, Switzerland
| | - Emanuele Menegatti
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Fiorenzo Artoni
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics and Institute of Bioengineering, Lausanne, Switzerland.,Functional Brain Mapping Laboratory, Department of Basic Neuroscience, Faculty of Medicine, University of Geneva, Geneva, Switzerland
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26
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Ortiz M, Ferrero L, Iáñez E, Azorín JM, Contreras-Vidal JL. Sensory Integration in Human Movement: A New Brain-Machine Interface Based on Gamma Band and Attention Level for Controlling a Lower-Limb Exoskeleton. Front Bioeng Biotechnol 2020; 8:735. [PMID: 33014987 PMCID: PMC7494737 DOI: 10.3389/fbioe.2020.00735] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 06/10/2020] [Indexed: 11/13/2022] Open
Abstract
Brain-machine interfaces (BMIs) can improve the control of assistance mobility devices making its use more intuitive and natural. In the case of an exoskeleton, they can also help rehabilitation therapies due to the reinforcement of neuro-plasticity through repetitive motor actions and cognitive engagement of the subject. Therefore, the cognitive implication of the user is a key aspect in BMI applications, and it is important to assure that the mental task correlates with the actual motor action. However, the process of walking is usually an autonomous mental task that requires a minimal conscious effort. Consequently, a brain-machine interface focused on the attention to gait could facilitate sensory integration in individuals with neurological impairment through the analysis of voluntary gait will and its repetitive use. This way the combined use of BMI+exoskeleton turns from assistance to restoration. This paper presents a new brain-machine interface based on the decoding of gamma band activity and attention level during motor imagery mental tasks. This work also shows a case study tested in able-bodied subjects prior to a future clinical study, demonstrating that a BMI based on gamma band and attention-level paradigm allows real-time closed-loop control of a Rex exoskeleton.
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Affiliation(s)
- Mario Ortiz
- Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, Elche, Spain.,Laboratory for Non-invasive Brain Machine Interfaces, Department of Electrical and Computer Engineering, University of Houston, Houston, TX, United States
| | - Laura Ferrero
- Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, Elche, Spain
| | - Eduardo Iáñez
- Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, Elche, Spain
| | - José M Azorín
- Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, Elche, Spain
| | - José L Contreras-Vidal
- Laboratory for Non-invasive Brain Machine Interfaces, Department of Electrical and Computer Engineering, University of Houston, Houston, TX, United States
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Nenna F, Do CT, Protzak J, Gramann K. Alteration of brain dynamics during dual-task overground walking. Eur J Neurosci 2020; 54:8158-8174. [PMID: 32881128 DOI: 10.1111/ejn.14956] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 08/25/2020] [Accepted: 08/26/2020] [Indexed: 11/29/2022]
Abstract
When walking in our natural environment, we often solve additional cognitive tasks. This increases the demand of resources needed for both the cognitive and motor systems, resulting in Cognitive-Motor Interference (CMI). A large portion of neurophysiological investigations on CMI took place in static settings, emphasizing the experimental rigor but overshadowing the ecological validity. As a more ecologically valid alternative to treadmill and desktop-based setups to investigate CMI, we developed a dual-task walking scenario in virtual reality (VR) combined with Mobile Brain/Body Imaging (MoBI). We aimed at investigating how brain dynamics are modulated by dual-task overground walking with an additional task in the visual domain. Participants performed a visual discrimination task in VR while standing (single-task) and walking overground (dual-task). Even though walking had no impact on the performance in the visual discrimination task, a P3 amplitude reduction along with changes in power spectral densities (PSDs) were observed for discriminating visual stimuli during dual-task walking. These results reflect an impact of walking on the parallel processing of visual stimuli even when the cognitive task is particularly easy. This standardized and easy to modify VR paradigm helps to systematically study CMI, allowing researchers to control for the impact of additional task complexity of tasks in different sensory modalities. Future investigations implementing an improved virtual design with more challenging cognitive and motor tasks will have to investigate the roles of both cognition and motion, allowing for a better understanding of the functional architecture of attention reallocation between cognitive and motor systems during active behavior.
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Affiliation(s)
- Federica Nenna
- Department of General Psychology, University of Padova, Padova, Italy
| | - Cao Tri Do
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Zurich, Switzerland
| | - Janna Protzak
- Junior research group FANS (Pedestrian Assistance System for Older Road User), Berlin Institute of Technology, Berlin, Germany
| | - Klaus Gramann
- Biological Psychology and Neuroergonomics, Berlin Institute of Technology, Berlin, Germany.,School of Computer Science, University of Technology Sydney, Sydney, NSW, Australia.,Center for Advanced Neurological Engineering, University of California, San Diego, CA, USA
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Naro A, Pignolo L, Sorbera C, Latella D, Billeri L, Manuli A, Portaro S, Bruschetta D, Calabrò RS. A Case-Controlled Pilot Study on Rhythmic Auditory Stimulation-Assisted Gait Training and Conventional Physiotherapy in Patients With Parkinson's Disease Submitted to Deep Brain Stimulation. Front Neurol 2020; 11:794. [PMID: 32849240 PMCID: PMC7417712 DOI: 10.3389/fneur.2020.00794] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Accepted: 06/25/2020] [Indexed: 01/13/2023] Open
Abstract
Deep brain stimulation (DBS) is indicated when motor disturbances in patients with idiopathic Parkinson's disease (PD) are refractory to current treatment options and significantly impair quality of life. However, post–DBS rehabilitation is essential, with particular regard to gait. Rhythmic auditory stimulation (RAS)-assisted treadmill gait rehabilitation within conventional physiotherapy program plays a major role in gait recovery. We explored the effects of a monthly RAS–assisted treadmill training within a conventional physiotherapy program on gait performance and gait-related EEG dynamics (while walking on the RAS–aided treadmill) in PD patients with (n = 10) and without DBS (n = 10). Patients with DBS achieved superior results than those without DBS concerning gait velocity, overall motor performance, and the timed velocity and self-confidence in balance, sit-to-stand (and vice versa) and walking, whereas both groups improved in dynamic and static balance, overall cognitive performance, and the fear of falling. The difference in motor outcomes between the two groups was paralleled by a stronger remodulation of gait cycle–related beta oscillations in patients with DBS as compared to those without DBS. Our work suggests that RAS-assisted gait training plus conventional physiotherapy is a useful strategy to improve gait performance in PD patients with and without DBS. Interestingly, patients with DBS may benefit more from this approach owing to a more focused and dynamic re–configuration of sensorimotor network beta oscillations related to gait secondary to the association between RAS-treadmill, conventional physiotherapy, and DBS. Actually, the coupling of these approaches may help restoring a residually altered beta–band response profile despite DBS intervention, thus better tailoring the gait rehabilitation of these PD patients.
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Affiliation(s)
- Antonino Naro
- IRCCS Centro Neurolesi Bonino Pulejo - Piemonte, Messina, Italy
| | - Loris Pignolo
- S. Anna Institute, Research in Advanced Neurorehabilitation (RAN), Crotone, Italy
| | - Chiara Sorbera
- IRCCS Centro Neurolesi Bonino Pulejo - Piemonte, Messina, Italy
| | - Desiree Latella
- IRCCS Centro Neurolesi Bonino Pulejo - Piemonte, Messina, Italy
| | - Luana Billeri
- IRCCS Centro Neurolesi Bonino Pulejo - Piemonte, Messina, Italy
| | - Alfredo Manuli
- IRCCS Centro Neurolesi Bonino Pulejo - Piemonte, Messina, Italy
| | - Simona Portaro
- IRCCS Centro Neurolesi Bonino Pulejo - Piemonte, Messina, Italy
| | - Daniele Bruschetta
- Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
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Tortora S, Ghidoni S, Chisari C, Micera S, Artoni F. Deep learning-based BCI for gait decoding from EEG with LSTM recurrent neural network. J Neural Eng 2020; 17:046011. [DOI: 10.1088/1741-2552/ab9842] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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30
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Jochumsen M, Niazi IK. Detection and classification of single-trial movement-related cortical potentials associated with functional lower limb movements. J Neural Eng 2020; 17:035009. [DOI: 10.1088/1741-2552/ab9a99] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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31
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Lennon O, Tonellato M, Del Felice A, Di Marco R, Fingleton C, Korik A, Guanziroli E, Molteni F, Guger C, Otner R, Coyle D. A Systematic Review Establishing the Current State-of-the-Art, the Limitations, and the DESIRED Checklist in Studies of Direct Neural Interfacing With Robotic Gait Devices in Stroke Rehabilitation. Front Neurosci 2020; 14:578. [PMID: 32714127 PMCID: PMC7344195 DOI: 10.3389/fnins.2020.00578] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 05/12/2020] [Indexed: 01/16/2023] Open
Abstract
Background: Stroke is a disease with a high associated disability burden. Robotic-assisted gait training offers an opportunity for the practice intensity levels associated with good functional walking outcomes in this population. Neural interfacing technology, electroencephalography (EEG), or electromyography (EMG) can offer new strategies for robotic gait re-education after a stroke by promoting more active engagement in movement intent and/or neurophysiological feedback. Objectives: This study identifies the current state-of-the-art and the limitations in direct neural interfacing with robotic gait devices in stroke rehabilitation. Methods: A pre-registered systematic review was conducted using standardized search operators that included the presence of stroke and robotic gait training and neural biosignals (EMG and/or EEG) and was not limited by study type. Results: From a total of 8,899 papers identified, 13 articles were considered for the final selection. Only five of the 13 studies received a strong or moderate quality rating as a clinical study. Three studies recorded EEG activity during robotic gait, two of which used EEG for BCI purposes. While demonstrating utility for decoding kinematic and EMG-related gait data, no EEG study has been identified to close the loop between robot and human. Twelve of the studies recorded EMG activity during or after robotic walking, primarily as an outcome measure. One study used multisource information fusion from EMG, joint angle, and force to modify robotic commands in real time, with higher error rates observed during active movement. A novel study identified used EMG data during robotic gait to derive the optimal, individualized robot-driven step trajectory. Conclusions: Wide heterogeneity in the reporting and the purpose of neurobiosignal use during robotic gait training after a stroke exists. Neural interfacing with robotic gait after a stroke demonstrates promise as a future field of study. However, as a nascent area, direct neural interfacing with robotic gait after a stroke would benefit from a more standardized protocol for biosignal collection and processing and for robotic deployment. Appropriate reporting for clinical studies of this nature is also required with respect to the study type and the participants' characteristics.
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Affiliation(s)
- Olive Lennon
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Michele Tonellato
- Department of Neuroscience, Rehabilitation Unit, University of Padova, Padova, Italy
| | - Alessandra Del Felice
- Department of Neuroscience, NEUROMOVE-Rehab Laboratory, University of Padova, Padova, Italy
- Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Roberto Di Marco
- Department of Neuroscience, NEUROMOVE-Rehab Laboratory, University of Padova, Padova, Italy
| | - Caitriona Fingleton
- Department of Physiotherapy, Mater Misericordiae University Hospital, Dublin, Ireland
| | - Attila Korik
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Derry, United Kingdom
| | | | - Franco Molteni
- Villa Beretta Rehabilitation Center, Valduce Hospital, Costa Masnaga, Italy
| | | | - Rupert Otner
- g.tec Medical Engineering GmbH, Schiedlberg, Austria
| | - Damien Coyle
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Derry, United Kingdom
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Berchicci M, Russo Y, Bianco V, Quinzi F, Rum L, Macaluso A, Committeri G, Vannozzi G, Di Russo F. Stepping forward, stepping backward: a movement-related cortical potential study unveils distinctive brain activities. Behav Brain Res 2020; 388:112663. [DOI: 10.1016/j.bbr.2020.112663] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 03/16/2020] [Accepted: 04/21/2020] [Indexed: 01/03/2023]
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Nakagome S, Luu TP, He Y, Ravindran AS, Contreras-Vidal JL. An empirical comparison of neural networks and machine learning algorithms for EEG gait decoding. Sci Rep 2020; 10:4372. [PMID: 32152333 PMCID: PMC7062700 DOI: 10.1038/s41598-020-60932-4] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 02/03/2020] [Indexed: 11/09/2022] Open
Abstract
Previous studies of Brain Computer Interfaces (BCI) based on scalp electroencephalography (EEG) have demonstrated the feasibility of decoding kinematics for lower limb movements during walking. In this computational study, we investigated offline decoding analysis with different models and conditions to assess how they influence the performance and stability of the decoder. Specifically, we conducted three computational decoding experiments that investigated decoding accuracy: (1) based on delta band time-domain features, (2) when downsampling data, (3) of different frequency band features. In each experiment, eight different decoder algorithms were compared including the current state-of-the-art. Different tap sizes (sample window sizes) were also evaluated for a real-time applicability assessment. A feature of importance analysis was conducted to ascertain which features were most relevant for decoding; moreover, the stability to perturbations was assessed to quantify the robustness of the methods. Results indicated that generally the Gated Recurrent Unit (GRU) and Quasi Recurrent Neural Network (QRNN) outperformed other methods in terms of decoding accuracy and stability. Previous state-of-the-art Unscented Kalman Filter (UKF) still outperformed other decoders when using smaller tap sizes, with fast convergence in performance, but occurred at a cost to noise vulnerability. Downsampling and the inclusion of other frequency band features yielded overall improvement in performance. The results suggest that neural network-based decoders with downsampling or a wide range of frequency band features could not only improve decoder performance but also robustness with applications for stable use of BCIs.
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Affiliation(s)
- Sho Nakagome
- Non-Invasive Brain Machine Interface Laboratory, Electrical and Computer Engineering Department, Houston, 77004, USA
| | - Trieu Phat Luu
- Non-Invasive Brain Machine Interface Laboratory, Electrical and Computer Engineering Department, Houston, 77004, USA
| | - Yongtian He
- Non-Invasive Brain Machine Interface Laboratory, Electrical and Computer Engineering Department, Houston, 77004, USA
| | - Akshay Sujatha Ravindran
- Non-Invasive Brain Machine Interface Laboratory, Electrical and Computer Engineering Department, Houston, 77004, USA
| | - Jose L Contreras-Vidal
- Non-Invasive Brain Machine Interface Laboratory, Electrical and Computer Engineering Department, Houston, 77004, USA.
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Sosnik R, Ben Zur O. Reconstruction of hand, elbow and shoulder actual and imagined trajectories in 3D space using EEG slow cortical potentials. J Neural Eng 2020; 17:016065. [DOI: 10.1088/1741-2552/ab59a7] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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35
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An J, Yoo D, Lee BC. Electrocortical activity changes in response to unpredictable trip perturbations induced by a split-belt treadmill. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:110-113. [PMID: 31945856 DOI: 10.1109/embc.2019.8856762] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This study explored the contributions of cortical activity in the primary sensorimotor cortex (SMC) and the posterior parietal cortex (PPC) to recovery responses following unpredictable trip perturbations. A technology platform equipped with a programmable split-belt treadmill induced unpredictable trip perturbations while walking. 128-channel non-invasive electroencephalography (EEG) signals were collected. Power spectral analysis was performed to quantify the electrocortical activity of two clusters in the SMC and PPC during quiet standing, steady state walking, and recovery periods. Alpha (8-13 Hz) power of the SMC and PPC was significantly suppressed during the recovery period compared to the standing and walking periods. The main finding of this study could inform the future development gait perturbation paradigms that facilitate the recovery responses in different populations, based on motor learning by repetition.
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36
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Weersink JB, Gefferie SR, van Laar T, Maurits NM, de Jong BM. Pre-Movement Cortico-Muscular Dynamics Underlying Improved Parkinson Gait Initiation after Instructed Arm Swing. JOURNAL OF PARKINSON'S DISEASE 2020; 10:1675-1693. [PMID: 32773398 PMCID: PMC7683047 DOI: 10.3233/jpd-202112] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 07/12/2020] [Indexed: 11/15/2022]
Abstract
BACKGROUND The supplementary motor area (SMA) is implicated in both motor initiation and stereotypic multi-limb movements such as walking with arm swing. Gait in Parkinson's disease exhibits starting difficulties and reduced arm swing, consistent with reduced SMA activity. OBJECTIVE We tested whether enhanced arm swing could improve Parkinson gait initiation and assessed whether increased SMA activity during preparation might facilitate such improvement. METHODS Effects of instructed arm swing on cortical activity, muscle activity and kinematics were assessed by ambulant EEG, EMG, accelerometers and video in 17 Parkinson patients and 19 controls. At baseline, all participants repeatedly started walking after a simple auditory cue. Next, patients started walking at this cue, which now meant starting with enhanced arm swing. EEG changes over the putative SMA and leg motor cortex were assessed by event related spectral perturbation (ERSP) analysis of recordings at Fz and Cz. RESULTS Over the putative SMA location (Fz), natural PD gait initiation showed enhanced alpha/theta synchronization around the auditory cue, and reduced alpha/beta desynchronization during gait preparation and movement onset, compared to controls. Leg muscle activity in patients was reduced during preparation and movement onset, while the latter was delayed compared to controls. When starting with enhanced arm swing, these group differences virtually disappeared. CONCLUSION Instructed arm swing improves Parkinson gait initiation. ERSP normalization around the cue indicates that the attributed information may serve as a semi-internal cue, recruiting an internalized motor program to overcome initiation difficulties.
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Affiliation(s)
- Joyce B. Weersink
- Department of Neurology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Silvano R. Gefferie
- Department of Neurology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Teus van Laar
- Department of Neurology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Natasha M. Maurits
- Department of Neurology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Bauke M. de Jong
- Department of Neurology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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Elvira M, Iáñez E, Quiles V, Ortiz M, Azorín JM. Pseudo-Online BMI Based on EEG to Detect the Appearance of Sudden Obstacles during Walking. SENSORS 2019; 19:s19245444. [PMID: 31835546 PMCID: PMC6960749 DOI: 10.3390/s19245444] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 11/08/2019] [Accepted: 12/05/2019] [Indexed: 12/03/2022]
Abstract
The aim of this paper is to describe new methods for detecting the appearance of unexpected obstacles during normal gait from EEG signals, improving the accuracy and reducing the false positive rate obtained in previous studies. This way, an exoskeleton for rehabilitation or assistance of people with motor limitations commanded by a Brain-Machine Interface (BMI) could be stopped in case that an obstacle suddenly appears during walking. The EEG data of nine healthy subjects were collected during their normal gait while an obstacle appearance was simulated by the projection of a laser line in a random pattern. Different approaches were considered for selecting the parameters of the BMI: subsets of electrodes, time windows and classifier probabilities, which were based on a linear discriminant analysis (LDA). The pseudo-online results of the BMI for detecting the appearance of obstacles, with an average percentage of 63.9% of accuracy and 2.6 false positives per minute, showed a significant improvement over previous studies.
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Papadimitriou A, Passalis N, Tefas A. Visual representation decoding from human brain activity using machine learning: A baseline study. Pattern Recognit Lett 2019. [DOI: 10.1016/j.patrec.2019.08.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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Korik A, Sosnik R, Siddique N, Coyle D. Decoding Imagined 3D Arm Movement Trajectories From EEG to Control Two Virtual Arms-A Pilot Study. Front Neurorobot 2019; 13:94. [PMID: 31798438 PMCID: PMC6868122 DOI: 10.3389/fnbot.2019.00094] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Accepted: 10/28/2019] [Indexed: 11/26/2022] Open
Abstract
Background: Realization of online control of an artificial or virtual arm using information decoded from EEG normally occurs by classifying different activation states or voluntary modulation of the sensorimotor activity linked to different overt actions of the subject. However, using a more natural control scheme, such as decoding the trajectory of imagined 3D arm movements to move a prosthetic, robotic, or virtual arm has been reported in a limited amount of studies, all using offline feed-forward control schemes. Objective: In this study, we report the first attempt to realize online control of two virtual arms generating movements toward three targets/arm in 3D space. The 3D trajectory of imagined arm movements was decoded from power spectral density of mu, low beta, high beta, and low gamma EEG oscillations using multiple linear regression. The analysis was performed on a dataset recorded from three subjects in seven sessions wherein each session comprised three experimental blocks: an offline calibration block and two online feedback blocks. Target classification accuracy using predicted trajectories of the virtual arms was computed and compared with results of a filter-bank common spatial patterns (FBCSP) based multi-class classification method involving mutual information (MI) selection and linear discriminant analysis (LDA) modules. Main Results: Target classification accuracy from predicted trajectory of imagined 3D arm movements in the offline runs for two subjects (mean 45%, std 5%) was significantly higher (p < 0.05) than chance level (33.3%). Nevertheless, the accuracy during real-time control of the virtual arms using the trajectory decoded directly from EEG was in the range of chance level (33.3%). However, the results of two subjects show that false-positive feedback may increase the accuracy in closed-loop. The FBCSP based multi-class classification method distinguished imagined movements of left and right arm with reasonable accuracy for two of the three subjects (mean 70%, std 5% compared to 50% chance level). However, classification of the imagined arm movement toward three targets was not successful with the FBCSP classifier as the achieved accuracy (mean 33%, std 5%) was similar to the chance level (33.3%). Sub-optimal components of the multi-session experimental paradigm were identified, and an improved paradigm proposed.
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Affiliation(s)
- Attila Korik
- Intelligent Systems Research Centre, Ulster University, Derry, United Kingdom
| | - Ronen Sosnik
- Hybrid BCI Lab, Holon Institute of Technology, Holon, Israel
| | - Nazmul Siddique
- Intelligent Systems Research Centre, Ulster University, Derry, United Kingdom
| | - Damien Coyle
- Intelligent Systems Research Centre, Ulster University, Derry, United Kingdom
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Wagner J, Martinez-Cancino R, Delorme A, Makeig S, Solis-Escalante T, Neuper C, Mueller-Putz G. High-density EEG mobile brain/body imaging data recorded during a challenging auditory gait pacing task. Sci Data 2019; 6:211. [PMID: 31624252 PMCID: PMC6797727 DOI: 10.1038/s41597-019-0223-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 09/06/2019] [Indexed: 02/07/2023] Open
Abstract
In this report we present a mobile brain/body imaging (MoBI) dataset that allows study of source-resolved cortical dynamics supporting coordinated gait movements in a rhythmic auditory cueing paradigm. Use of an auditory pacing stimulus stream has been recommended to identify deficits and treat gait impairments in neurologic populations. Here, the rhythmic cueing paradigm required healthy young participants to walk on a treadmill (constant speed) while attempting to maintain step synchrony with an auditory pacing stream and to adapt their step length and rate to unanticipated shifts in tempo of the pacing stimuli (e.g., sudden shifts to a faster or slower tempo). High-density electroencephalography (EEG, 108 channels), surface electromyography (EMG, bilateral tibialis anterior), pressure sensors on the heel (to register timing of heel strikes), and goniometers (knee, hip, and ankle joint angles) were concurrently recorded in 20 participants. The data is provided in the Brain Imaging Data Structure (BIDS) format to promote data sharing and reuse, and allow the inclusion of the data into fully automated data analysis workflows.
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Affiliation(s)
- Johanna Wagner
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA, USA.
- Laboratory for Brain Computer Interfaces, Institute of Neural Engineering, Graz University of Technology, Graz, Austria.
| | - Ramon Martinez-Cancino
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA, USA
- Electric and Computer Engineering Department, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
| | - Arnaud Delorme
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA, USA
| | - Scott Makeig
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA, USA
| | - Teodoro Solis-Escalante
- Laboratory for Brain Computer Interfaces, Institute of Neural Engineering, Graz University of Technology, Graz, Austria
- Department of Rehabilitation, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Christa Neuper
- Laboratory for Brain Computer Interfaces, Institute of Neural Engineering, Graz University of Technology, Graz, Austria
- Department of Psychology, University of Graz, Graz, Austria
| | - Gernot Mueller-Putz
- Laboratory for Brain Computer Interfaces, Institute of Neural Engineering, Graz University of Technology, Graz, Austria
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Whittier T, Willy RW, Sandri Heidner G, Niland S, Melton C, Mizelle JC, Murray NP. The Cognitive Demands of Gait Retraining in Runners: An EEG Study. J Mot Behav 2019; 52:360-371. [PMID: 31328698 DOI: 10.1080/00222895.2019.1635983] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
High impact forces during running have been associated with tibial stress injuries. Previous research has demonstrated increasing step rate will decrease impact forces during running. However, no research has determined the cognitive demand of gait retraining. The primary purpose was to determine the cognitive demand and effectiveness of field-based gait retraining. We hypothesized that in-field gait retraining would alter running mechanics without increasing cognitive workload as measured by EEG following learning. Runners with a history of tibial injury completed a gait retraining protocol which included a baseline run, retraining phase, practice phase, and re-assessment following retraining protocol. Results demonstrated an increase in the theta, beta, and gamma power within prefrontal cortex during new learning and corresponding return to baseline following skill acquisition and changes across alpha, beta, gamma, mu, and theta in the motor cortex (p < .05). In the midline superior parietal cortex, spectral power was greater for theta activity during new learning with a corresponding alpha suppression. Overall, the results demonstrated the use of EEG as an effective tool to measure cognitive demand for implicit motor learning and the effectiveness of in-field gait retraining.
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Affiliation(s)
| | - Richard W Willy
- School of Physical Therapy and Rehabilitation Science, University of Montana, Missoula, Montana, USA
| | | | - Samantha Niland
- 3Department of Kinesiology, East Carolina University, Greenville, North Carolina, USA
| | - Caitlin Melton
- 3Department of Kinesiology, East Carolina University, Greenville, North Carolina, USA
| | - J C Mizelle
- 3Department of Kinesiology, East Carolina University, Greenville, North Carolina, USA
| | - Nicholas P Murray
- 3Department of Kinesiology, East Carolina University, Greenville, North Carolina, USA
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Abstract
OBJECTIVE Advancements in robot-assisted gait rehabilitation and brain-machine interfaces may enhance stroke physiotherapy by engaging patients while providing information about robot-induced cortical adaptations. We investigate the feasibility of decoding walking from brain activity in stroke survivors during therapy using a powered exoskeleton integrated with an electroencephalography-based brain-machine interface. DESIGN The H2 powered exoskeleton was designed for overground gait training with actuated hip, knee, and ankle joints. It was integrated with active-electrode electroencephalography and evaluated in hemiparetic stroke survivors for 12 sessions per 4 wks. A continuous-time Kalman decoder operating on delta-band electroencephalography was designed to estimate gait kinematics. RESULTS Five chronic stroke patients completed the study with improvements in walking distance and speed training for 4 wks, correlating with increased offline decoding accuracy. Accuracies of predicted joint angles improved with session and gait speed, suggesting an improved neural representation for gait, and the feasibility to design an electroencephalography-based brain-machine interface to monitor brain activity or control a rehabilitative exoskeleton. CONCLUSIONS The Kalman decoder showed increased accuracies as the longitudinal training intervention progressed in the stroke participants. These results demonstrate the feasibility of studying changes in patterns of neuroelectric cortical activity during poststroke rehabilitation and represent the first step in developing a brain-machine interface for controlling powered exoskeletons.
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Rahman M, Karwowski W, Fafrowicz M, Hancock PA. Neuroergonomics Applications of Electroencephalography in Physical Activities: A Systematic Review. Front Hum Neurosci 2019; 13:182. [PMID: 31214002 PMCID: PMC6558147 DOI: 10.3389/fnhum.2019.00182] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Accepted: 05/20/2019] [Indexed: 11/13/2022] Open
Abstract
Recent years have seen increased interest in neuroergonomics, which investigates the brain activities of people engaged in diverse physical and cognitive activities at work and in everyday life. The present work extends upon prior assessments of the state of this art. However, here we narrow our focus specifically to studies that use electroencephalography (EEG) to measure brain activity, correlates, and effects during physical activity. The review uses systematically selected, openly published works derived from a guided search through peer-reviewed journals and conference proceedings. Identified studies were then categorized by the type of physical activity and evaluated considering methodological and chronological aspects via statistical and content-based analyses. From the identified works (n = 110), a specific number (n = 38) focused on less mobile muscular activities, while an additional group (n = 22) featured both physical and cognitive tasks. The remainder (n = 50) investigated various physical exercises and sporting activities and thus were here identified as a miscellaneous grouping. Most of the physical activities were isometric exertions, moving parts of upper and lower limbs, or walking and cycling. These primary categories were sub-categorized based on movement patterns, the use of the event-related potentials (ERP) technique, the use of recording methods along with EEG and considering mental effects. Further information on subjects' gender, EEG recording devices, data processing, and artifact correction methods and citations was extracted. Due to the heterogeneous nature of the findings from various studies, statistical analyses were not performed. These were thus included in a descriptive fashion. Finally, contemporary research gaps were pointed out, and future research prospects to address those gaps were discussed.
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Affiliation(s)
- Mahjabeen Rahman
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Waldemar Karwowski
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Magdalena Fafrowicz
- Department of Cognitive Neuroscience and Neuroergonomics, Neurobiology Department, The Maloploska Center of Biotechnology, Jagiellonian University in Krakow, Krakow, Poland
| | - Peter A Hancock
- Department of Psychology, University of Central Florida, Orlando, FL, United States
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Barios JA, Ezquerro S, Bertomeu-Motos A, Nann M, Badesa FJ, Fernandez E, Soekadar SR, Garcia-Aracil N. Synchronization of Slow Cortical Rhythms During Motor Imagery-Based Brain–Machine Interface Control. Int J Neural Syst 2019; 29:1850045. [DOI: 10.1142/s0129065718500454] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Modulation of sensorimotor rhythm (SMR) power, a rhythmic brain oscillation physiologically linked to motor imagery, is a popular Brain–Machine Interface (BMI) paradigm, but its interplay with slower cortical rhythms, also involved in movement preparation and cognitive processing, is not entirely understood. In this study, we evaluated the changes in phase and power of slow cortical activity in delta and theta bands, during a motor imagery task controlled by an SMR-based BMI system. In Experiment I, EEG of 20 right-handed healthy volunteers was recorded performing a motor-imagery task using an SMR-based BMI controlling a visual animation, and during task-free intervals. In Experiment II, 10 subjects were evaluated along five daily sessions, while BMI-controlling same visual animation, a buzzer, and a robotic hand exoskeleton. In both experiments, feedback received from the controlled device was proportional to SMR power (11–14[Formula: see text]Hz) detected by a real-time EEG-based system. Synchronization of slow EEG frequencies along the trials was evaluated using inter-trial-phase coherence (ITPC). Results: cortical oscillations of EEG in delta and theta frequencies synchronized at the onset and at the end of both active and task-free trials; ITPC was significantly modulated by feedback sensory modality received during the tasks; and ITPC synchronization progressively increased along the training. These findings suggest that phase-locking of slow rhythms and resetting by sensory afferences might be a functionally relevant mechanism in cortical control of motor function. We propose that analysis of phase synchronization of slow cortical rhythms might also improve identification of temporal edges in BMI tasks and might help to develop physiological markers for identification of context task switching and practice-related changes in brain function, with potentially important implications for design and monitoring of motor imagery-based BMI systems, an emerging tool in neurorehabilitation of stroke.
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Affiliation(s)
- Juan A. Barios
- Biomedical Neuroengineering Research Group (nBio), Systems Engineering and Automation, Department of Miguel Hernández University, Avda. de la Universidad s/n 03202 Elche, Spain
| | - Santiago Ezquerro
- Biomedical Neuroengineering Research Group (nBio), Systems Engineering and Automation, Department of Miguel Hernández University, Avda. de la Universidad s/n 03202 Elche, Spain
| | - Arturo Bertomeu-Motos
- Biomedical Neuroengineering Research Group (nBio), Systems Engineering and Automation, Department of Miguel Hernández University, Avda. de la Universidad s/n 03202 Elche, Spain
| | - Marius Nann
- University Hospital of Tuebingen, Applied Neurotechnology Lab, Department of Psychiatry and Psychotherapy, Calwerstr. 14, 72076 Tuebingen, Germany
| | - Fco. Javier Badesa
- Biomedical Neuroengineering Research Group (nBio), Systems Engineering and Automation, Department of Miguel Hernández University, Avda. de la Universidad s/n 03202 Elche, Spain
| | - Eduardo Fernandez
- Biomedical Neuroengineering Research Group (nBio), Systems Engineering and Automation, Department of Miguel Hernández University, Avda. de la Universidad s/n 03202 Elche, Spain
| | - Surjo R. Soekadar
- University Hospital of Tuebingen, Applied Neurotechnology Lab, Department of Psychiatry and Psychotherapy, Calwerstr. 14, 72076 Tuebingen, Germany
- Clinical Neurotechnology Laboratory, Neuroscience Research Center (NWFZ), Department of Psychiatry and Psychotherapy, Charite University Medicine Berlin, Berlin, Germany
| | - Nicolas Garcia-Aracil
- Biomedical Neuroengineering Research Group (nBio), Systems Engineering and Automation, Department of Miguel Hernández University, Avda. de la Universidad s/n 03202 Elche, Spain
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Configuration of electrical spinal cord stimulation through real-time processing of gait kinematics. Nat Protoc 2019; 13:2031-2061. [PMID: 30190556 DOI: 10.1038/s41596-018-0030-9] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Epidural electrical stimulation (EES) of the spinal cord and real-time processing of gait kinematics are powerful methods for the study of locomotion and the improvement of motor control after injury or in neurological disorders. Here, we describe equipment and surgical procedures that can be used to acquire chronic electromyographic (EMG) recordings from leg muscles and to implant targeted spinal cord stimulation systems that remain stable up to several months after implantation in rats and nonhuman primates. We also detail how to exploit these implants to configure electrical spinal cord stimulation policies that allow control over the degree of extension and flexion of each leg during locomotion. This protocol uses real-time processing of gait kinematics and locomotor performance, and can be configured within a few days. Once configured, stimulation bursts are delivered over specific spinal cord locations with precise timing that reproduces the natural spatiotemporal activation of motoneurons during locomotion. These protocols can also be easily adapted for the safe implantation of systems in the vicinity of the spinal cord and to conduct experiments involving real-time movement feedback and closed-loop controllers.
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Yokoyama H, Kaneko N, Ogawa T, Kawashima N, Watanabe K, Nakazawa K. Cortical Correlates of Locomotor Muscle Synergy Activation in Humans: An Electroencephalographic Decoding Study. iScience 2019; 15:623-639. [PMID: 31054838 PMCID: PMC6547791 DOI: 10.1016/j.isci.2019.04.008] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Revised: 02/09/2019] [Accepted: 04/04/2019] [Indexed: 01/17/2023] Open
Abstract
Muscular control during walking is believed to be simplified by the coactivation of muscles called muscle synergies. Although significant corticomuscular connectivity during walking has been reported, the level at which the cortical activity is involved in muscle activity (muscle synergy or individual muscle level) remains unclear. Here we examined cortical correlates of muscle activation during walking by brain decoding of activation of muscle synergies and individual muscles from electroencephalographic signals. We demonstrated that the activation of locomotor muscle synergies was decoded from slow cortical waves. In addition, the decoding accuracy for muscle synergies was greater than that for individual muscles and the decoding of individual muscle activation was based on muscle-synergy-related cortical information. These results indicate the cortical correlates of locomotor muscle synergy activation. These findings expand our understanding of the relationships between brain and locomotor muscle synergies and could accelerate the development of effective brain-machine interfaces for walking rehabilitation.
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Affiliation(s)
- Hikaru Yokoyama
- Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, Koganei-shi, Tokyo 184-8588, Japan; Japan Society for the Promotion of Science, Chiyoda-ku, Tokyo 102-0083, Japan
| | - Naotsugu Kaneko
- Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
| | - Tetsuya Ogawa
- Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
| | - Noritaka Kawashima
- Department of Rehabilitation for the Movement Functions, Research Institute of National Rehabilitation Center for the Disabled, Tokorozawa-shi, Saitama 359-0042, Japan
| | - Katsumi Watanabe
- Faculty of Science and Engineering, Waseda University, Shinjuku-ku Tokyo 169-8555, Japan; Art & Design, University of New South Wales, Sydney, NSW 2021, Australia; Faculty of Kinesiology and Physical Education, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Kimitaka Nakazawa
- Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan.
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Shakibaee F, Mottaghi E, Kobravi HR, Ghoshuni M. Decoding knee angle trajectory from electroencephalogram signal using NARX neural network and a new channel selection algorithm. Biomed Phys Eng Express 2019. [DOI: 10.1088/2057-1976/aafd48] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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48
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Varghese JP, Staines WR, McIlroy WE. Activity in Functional Cortical Networks Temporally Associated with Postural Instability. Neuroscience 2019; 401:43-58. [PMID: 30668974 DOI: 10.1016/j.neuroscience.2019.01.008] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2018] [Revised: 12/05/2018] [Accepted: 01/11/2019] [Indexed: 12/28/2022]
Abstract
Human bipedal balance control is proposed to be the integrated activity of distributed neural areas. There is growing understanding about the cortical involvement in this highly automated behavior. While evidence exists for cortical activity temporally linked to reactive balance control, little is known about the functional interaction of potential cortical regions. Here, we used functional connectivity and graph theoretical analysis to derive functional cortical networks during reactive balance control from an event-related potential evoked by external perturbation known as the perturbation-evoked potential N1 (PEP N1). Fourteen healthy young adults were subjected to temporally unpredictable postural perturbations using a custom-made lean and release cable system. Electroencephalographic signals were recorded using a 64-channel electrode cap and segmented around perturbation onset. Functional connectivity was analyzed in source-space and sensor-space using coherence measures and functional cortical networks were characterized using graph measures. The results suggest that there might exist a balance control cortical network while standing and rapid, transient, and frequency-specific reorganization occurs in this network during reactive balance control events. This reorganization was characterized by an increased number of short-range connections between neighboring areas and increased strength between connections in delta, theta, alpha, and beta frequency bands during PEP N1 compared to baseline. To our knowledge, this is the first study to report the existence of functional cortical networks during reactive balance control with potential implications on assessing impaired balance associated with various neural diseases.
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Affiliation(s)
- Jessy Parokaran Varghese
- Department of Kinesiology, University of Waterloo, 200 University Ave W, Waterloo, Ontario N2L 3G1, Canada
| | - William R Staines
- Department of Kinesiology, University of Waterloo, 200 University Ave W, Waterloo, Ontario N2L 3G1, Canada
| | - William E McIlroy
- Department of Kinesiology, University of Waterloo, 200 University Ave W, Waterloo, Ontario N2L 3G1, Canada; Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, 2075 Bayview Avenue, Toronto, Ontario ON M4N 3M5, Canada.
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Delta and alpha rhythms are modulated by the physical movement knowledge in acrobatic gymnastics: an EEG study in visual context. SPORT SCIENCES FOR HEALTH 2018. [DOI: 10.1007/s11332-018-0461-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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50
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Mercado L, Azorin JM, Platas M, Ubeda A, Quiroz G. Offline Lower-Limb Kinematic Decodification by Segments of EEG Signals. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:2398-2401. [PMID: 30440890 DOI: 10.1109/embc.2018.8512840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
In this work, hip and knee angles were decoded from low frequency EEG components recorded during the execution of two tasks. In order to compare their performance, three decoders based on multiple linear regression (MLR) models were applied under different conditions; which consisted in considering the processed data as a whole or divided into segments. Results suggest that, when the segments are related to specific tasks, the segmentation provides a better performance than applying the decoding method to unsegmented data.
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