51
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Saha S, Baumert M. Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review. Front Comput Neurosci 2020; 13:87. [PMID: 32038208 PMCID: PMC6985367 DOI: 10.3389/fncom.2019.00087] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 12/16/2019] [Indexed: 12/05/2022] Open
Abstract
Brain computer interfaces (BCI) for the rehabilitation of motor impairments exploit sensorimotor rhythms (SMR) in the electroencephalogram (EEG). However, the neurophysiological processes underpinning the SMR often vary over time and across subjects. Inherent intra- and inter-subject variability causes covariate shift in data distributions that impede the transferability of model parameters amongst sessions/subjects. Transfer learning includes machine learning-based methods to compensate for inter-subject and inter-session (intra-subject) variability manifested in EEG-derived feature distributions as a covariate shift for BCI. Besides transfer learning approaches, recent studies have explored psychological and neurophysiological predictors as well as inter-subject associativity assessment, which may augment transfer learning in EEG-based BCI. Here, we highlight the importance of measuring inter-session/subject performance predictors for generalized BCI frameworks for both normal and motor-impaired people, reducing the necessity for tedious and annoying calibration sessions and BCI training.
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Affiliation(s)
- Simanto Saha
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia
| | - Mathias Baumert
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia
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52
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Fan J, Milosevic R, Li J, Bai J, Zhang Y. The impact of neuroimaging advancement on neurocognitive evaluation in pediatric brain tumor survivors: A review. BRAIN SCIENCE ADVANCES 2020. [DOI: 10.1177/2096595820902565] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Pediatric brain tumors are a type of tumors that are commonly present in children and young adults. With the improvement of treatment, the quality of life, especially the cognitive functioning, is gaining increasingly more attention. Apart from cognitive evaluations, neuroimaging studies begin to play an important part in neurocognitive functioning investigation. In this way, the brain tissue changes caused by tumor variables (including tumor location and tumor size) and treatment variables (including surgery, chemotherapy and radiotherapy) can be detected by neuroimaging. Recent advancement of neuroimaging techniques, such as functional-MRI (fMRI) and diffusion tensor imaging (DTI), made great contributions to understanding cognitive dysfunction and quantifying the effects of tumor variables and treatment variables. In recent years, laminar-fMRI provided a potentially valuable tool for examining the exact origins of neural activity and cognitive function. On the other hand, molecular fMRI might guide diagnosis and treatment of brain disease in the future by using new biomarkers, and DTI can detect white matter changes and obtain some anatomically specific information.
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Affiliation(s)
- Juan Fan
- Yuquan Hosipital, Tsinghua University, Beijing 100040, China
| | | | - Jiefei Li
- Yuquan Hosipital, Tsinghua University, Beijing 100040, China
| | - Jianjun Bai
- Yuquan Hosipital, Tsinghua University, Beijing 100040, China
| | - Yuqi Zhang
- Yuquan Hosipital, Tsinghua University, Beijing 100040, China
- School of Medicine, Tsinghua University, Beijing 100084, China
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53
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Abstract
The electroencephalogram (EEG) was invented almost 100 years ago and is still a method of choice for many research questions, even applications-from functional brain imaging in neuroscientific investigations during movement to real-time applications like brain-computer interfacing. This chapter gives some background information on the establishment and properties of the EEG. This chapter starts with a closer look at the sources of EEG at a micro or neuronal level, followed by recording techniques, types of electrodes, and common EEG artifacts. Then an overview on EEG phenomena, namely, spontaneous EEG and event-related potentials build the middle part of this chapter. The last part discusses brain signals, which are used in current BCI research, including short descriptions and examples of applications.
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Affiliation(s)
- Gernot R Müller-Putz
- Institute for Neural Engineering, Laboratory of Brain-Computer Interfaces, Graz University of Technology, Graz, Austria.
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54
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Influence of focal vibration over Achilles tendon on the activation of sensorimotor cortex in healthy subjects and subacute stroke patients. Neuroreport 2019; 30:1081-1086. [PMID: 31503206 DOI: 10.1097/wnr.0000000000001319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
The modulation of cerebral activity could induce plastic changes in the cerebral cortex and contribute to motor rehabilitation. Focal vibration over lower-extremity muscles has therapeutic effects on the impaired motor function for stroke patients, but the modulatory effects of focal vibration on brain activity are less known. To explore this problem, this experiment was designed and conducted, in which focal vibration (75 Hz) was applied over the right Achilles tendon of 14 healthy subjects and the affected Achilles tendon of seven subacute stroke patients. Electroencephalography was recorded in the following phases: resting-state and three focal vibration sessions. Electroencephalographical analysis showed a significantly desynchronized power of contralateral primary sensorimotor cortex (S1-M1) in beta1 band (13-18 Hz) following all focal vibration sessions occurred in healthy subjects compared to resting-state, whereas a significantly desynchronized power of bilateral S1-M1 in the beta1 and beta2 band (18-21 Hz) was observed in stroke patients compared to resting-state. Besides, event-related power desynchronization of bilateral S1-M1 in stroke patients was significantly lower than healthy subjects in the beta2 and beta3 band (21-30 Hz) during focal vibration sessions. These results demonstrated that focal vibration over Achilles tendon could activate bilateral S1-M1 in stroke patients, which was different in healthy subjects. These indications contribute to a better understanding of the underlying mechanism of focal vibration on stroke rehabilitation.
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55
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Li W, Li C, Xiang Y, Ji L, Hu H, Liu Y. Study of the activation in sensorimotor cortex and topological properties of functional brain network following focal vibration on healthy subjects and subacute stroke patients: An EEG study. Brain Res 2019; 1722:146338. [PMID: 31323197 DOI: 10.1016/j.brainres.2019.146338] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Revised: 07/12/2019] [Accepted: 07/15/2019] [Indexed: 12/16/2022]
Abstract
Modulation on cerebral cortex and cerebral networks can induce reorganization of the brain, which contributes to rehabilitation. Previous studies have proved that focal vibration (FV) on limb muscles can modulate the activities of sensorimotor cortex in healthy subjects (HS). The objective of this paper is to study the modulatory effects of FV on the sensorimotor cortex and cerebral network in HS and subacute stroke patients (SP). An experiment was designed and conducted, during which FV of 75 Hz was applied over biceps muscle of right limb of 10 HS and 10 SP with right hemiplegia. Electroencephalography (EEG) was recorded in the following phases: before FV, control condition and three sessions of FV. EEG analysis showed a significant decrease in motor-related power desynchronization (MRPD) of contralesional primary sensorimotor cortex (contralesional S1-M1) in the beta2 band (18-21 Hz) for SP during FV sessions, as well as in MRPD of bilateral S1-M1 in the beta1 (13-18 Hz) and the beta2 band for HS. Moreover, MRPD of contralesional S1-M1 was significantly lower than MRPD of ipsilesional S1-M1 during FV. Besides, a significant increase of global efficiency (E) and decrease of characteristic path length (L) were identified in the beta1 band for SP, whereas a significant increase of L was identified for HS. The results indicated that FV could enhance the excitability of contralesional S1-M1 and alter topological properties of functional brain network for SP, which was different in HS. This indication can contribute to understanding the modulatory effects of FV on cerebral cortex and cerebral network.
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Affiliation(s)
- Wei Li
- Division of Intelligent and Biomechanical System, State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Haidian, Beijing, China.
| | - Chong Li
- Division of Intelligent and Biomechanical System, State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Haidian, Beijing, China.
| | - Yun Xiang
- Department of Rehabilitation Medicine, Shenzhen Nanshan People's Hospital and the 6th Affiliated Hospital of Shenzhen University Health Science Center, China
| | - Linhong Ji
- Division of Intelligent and Biomechanical System, State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Haidian, Beijing, China.
| | - Hui Hu
- Department of Rehabilitation Medicine, Shenzhen Nanshan People's Hospital and the 6th Affiliated Hospital of Shenzhen University Health Science Center, China
| | - Yali Liu
- Department of Mechanical and Electrical Engineering, Beijing Institute of Technology, Haidian, Beijing, China
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56
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Zuo C, Jin J, Yin E, Saab R, Miao Y, Wang X, Hu D, Cichocki A. Novel hybrid brain-computer interface system based on motor imagery and P300. Cogn Neurodyn 2019; 14:253-265. [PMID: 32226566 DOI: 10.1007/s11571-019-09560-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 09/19/2019] [Accepted: 10/08/2019] [Indexed: 01/08/2023] Open
Abstract
Motor imagery (MI) is a mental representation of motor behavior and has been widely used in electroencephalogram based brain-computer interfaces (BCIs). Several studies have demonstrated the efficacy of MI-based BCI-feedback training in post-stroke rehabilitation. However, in the earliest stage of the training, calibration data typically contain insufficient discriminability, resulting in unreliable feedback, which may decrease subjects' motivation and even hinder their training. To improve the performance in the early stages of MI training, a novel hybrid BCI paradigm based on MI and P300 is proposed in this study. In this paradigm, subjects are instructed to imagine writing the Chinese character following the flash order of the desired Chinese character displayed on the screen. The event-related desynchronization/synchronization (ERD/ERS) phenomenon is produced with writing based on one's imagination. Simultaneously, the P300 potential is evoked by the flash of each stroke. Moreover, a fusion method of P300 and MI classification is proposed, in which unreliable P300 classifications are corrected by reliable MI classifications. Twelve healthy naïve MI subjects participated in this study. Results demonstrated that the proposed hybrid BCI paradigm yielded significantly better performance than the single-modality BCI paradigm. The recognition accuracy of the fusion method is significantly higher than that of P300 (p < 0.05) and MI (p < 0.01). Moreover, the training data size can be reduced through fusion of these two modalities.
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Affiliation(s)
- Cili Zuo
- 1Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Jing Jin
- 1Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Erwei Yin
- Unmanned Systems Research Center, National Institute of Defense Technology Innovation, Academy of Military Sciences China, Beijing, 100081 People's Republic of China.,Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin, People's Republic of China
| | - Rami Saab
- 4Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada
| | - Yangyang Miao
- 1Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Xingyu Wang
- 1Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Dewen Hu
- 5College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha, 410073 Hunan People's Republic of China
| | - Andrzej Cichocki
- 6Skolkovo Institute of Science and Technology (SKOLTECH), Moscow, Russia 143026.,7Systems Research Institute PAS, Warsaw, Poland.,8Nicolaus Copernicus University (UMK), Torun, Poland
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57
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Mele G, Cavaliere C, Alfano V, Orsini M, Salvatore M, Aiello M. Simultaneous EEG-fMRI for Functional Neurological Assessment. Front Neurol 2019; 10:848. [PMID: 31456735 PMCID: PMC6700249 DOI: 10.3389/fneur.2019.00848] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Accepted: 07/22/2019] [Indexed: 01/05/2023] Open
Abstract
The increasing incidence of neurodegenerative and psychiatric diseases requires increasingly sophisticated tools for their diagnosis and monitoring. Clinical assessment takes advantage of objective parameters extracted by electroencephalogram and magnetic resonance imaging (MRI) among others, to support clinical management of neurological diseases. The complementarity of these two tools can be now emphasized by the possibility of integrating the two technologies in a hybrid solution, allowing simultaneous acquisition of the two signals by the novel EEG-fMRI technology. This review will focus on simultaneous EEG-fMRI technology and related early studies, dealing about issues related to the acquisition and processing of simultaneous signals, and including critical discussion about clinical and technological perspectives.
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58
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Fan J, Milosevic R, Li J, Bai J, Zhang Y. The impact of neuroimaging advancement on neurocognitive evaluation in pediatric brain tumor survivors: A review. BRAIN SCIENCE ADVANCES 2019. [DOI: 10.26599/bsa.2019.9050008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
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59
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Effects of Focal Vibration over Upper Limb Muscles on the Activation of Sensorimotor Cortex Network: An EEG Study. JOURNAL OF HEALTHCARE ENGINEERING 2019; 2019:9167028. [PMID: 31263527 PMCID: PMC6556786 DOI: 10.1155/2019/9167028] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Revised: 04/08/2019] [Accepted: 05/12/2019] [Indexed: 12/19/2022]
Abstract
Studying the therapeutic effects of focal vibration (FV) in neurorehabilitation is the focus of current research. However, it is still not fully understood how FV on upper limb muscles affects the sensorimotor cortex in healthy subjects. To explore this problem, this experiment was designed and conducted, in which FV was applied to the muscle belly of biceps brachii in the left arm. During the experiment, electroencephalography (EEG) was recorded in the following three phases: before FV, during FV, and two minutes after FV. During FV, a significant lower relative power at C3 and C4 electrodes and a significant higher connection strength between five channel pairs (Cz-FC1, Cz-C3, Cz-CP6, C4-FC6, and FC6-CP2) in the alpha band were observed compared to those before FV. After FV, the relative power at C4 in the beta band showed a significant increase compared to its value before FV. The changes of the relative power at C4 in the alpha band had a negative correlation with the relative power of the beta band during FV and with that after FV. The results showed that FV on upper limb muscles could activate the bilateral primary somatosensory cortex and strengthen functional connectivity of the ipsilateral central area (FC1, C3, and Cz) and contralateral central area (CP2, Cz, C4, FC6, and CP6). These results contribute to understanding the effect of FV over upper limb muscles on the brain cortical network.
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60
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Mikkelsen KB, Ebajemito JK, Bonmati‐Carrion MA, Santhi N, Revell VL, Atzori G, della Monica C, Debener S, Dijk D, Sterr A, de Vos M. Machine-learning-derived sleep-wake staging from around-the-ear electroencephalogram outperforms manual scoring and actigraphy. J Sleep Res 2019; 28:e12786. [PMID: 30421469 PMCID: PMC6446944 DOI: 10.1111/jsr.12786] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2018] [Revised: 09/23/2018] [Accepted: 10/05/2018] [Indexed: 12/22/2022]
Abstract
Quantification of sleep is important for the diagnosis of sleep disorders and sleep research. However, the only widely accepted method to obtain sleep staging is by visual analysis of polysomnography (PSG), which is expensive and time consuming. Here, we investigate automated sleep scoring based on a low-cost, mobile electroencephalogram (EEG) platform consisting of a lightweight EEG amplifier combined with flex-printed cEEGrid electrodes placed around the ear, which can be implemented as a fully self-applicable sleep system. However, cEEGrid signals have different amplitude characteristics to normal scalp PSG signals, which might be challenging for visual scoring. Therefore, this study evaluates the potential of automatic scoring of cEEGrid signals using a machine learning classifier ("random forests") and compares its performance with manual scoring of standard PSG. In addition, the automatic scoring of cEEGrid signals is compared with manual annotation of the cEEGrid recording and with simultaneous actigraphy. Acceptable recordings were obtained in 15 healthy volunteers (aged 35 ± 14.3 years) during an extended nocturnal sleep opportunity, which induced disrupted sleep with a large inter-individual variation in sleep parameters. The results demonstrate that machine-learning-based scoring of around-the-ear EEG outperforms actigraphy with respect to sleep onset and total sleep time assessments. The automated scoring outperforms human scoring of cEEGrid by standard criteria. The accuracy of machine-learning-based automated scoring of cEEGrid sleep recordings compared with manual scoring of standard PSG was satisfactory. The findings show that cEEGrid recordings combined with machine-learning-based scoring holds promise for large-scale sleep studies.
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Affiliation(s)
- Kaare B. Mikkelsen
- Institute of Biomedical EngineeringUniversity of OxfordOxfordUK
- Department of EngineeringAarhus UniversityAarhusDenmark
| | | | | | | | | | | | | | - Stefan Debener
- Cluster of Excellence Hearing4AllOldenburgGermany
- Department of PsychologyUniversity of OldenburgOldenburgGermany
| | - Derk‐Jan Dijk
- Surrey Sleep Research CentreUniversity of SurreySurreyUK
- Surrey Clinical Research CentreUniversity of SurreySurreyUK
| | | | - Maarten de Vos
- Institute of Biomedical EngineeringUniversity of OxfordOxfordUK
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61
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Padfield N, Zabalza J, Zhao H, Masero V, Ren J. EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges. SENSORS (BASEL, SWITZERLAND) 2019; 19:E1423. [PMID: 30909489 PMCID: PMC6471241 DOI: 10.3390/s19061423] [Citation(s) in RCA: 157] [Impact Index Per Article: 31.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 03/10/2019] [Accepted: 03/19/2019] [Indexed: 12/11/2022]
Abstract
Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs.
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Affiliation(s)
- Natasha Padfield
- Centre for Signal and Image Processing, University of Strathclyde, Glasgow G1 1XW, UK.
| | - Jaime Zabalza
- Centre for Signal and Image Processing, University of Strathclyde, Glasgow G1 1XW, UK.
| | - Huimin Zhao
- School of Computer Sciences, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
- The Guangzhou Key Laboratory of Digital Content Processing and Security Technologies, Guangzhou 510665, China.
| | - Valentin Masero
- Department of Computer Systems and Telematics Engineering, Universidad de Extremadura, 06007 Badajoz, Spain.
| | - Jinchang Ren
- Centre for Signal and Image Processing, University of Strathclyde, Glasgow G1 1XW, UK.
- School of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China.
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62
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Meekes J, Debener S, Zich C, Bleichner MG, Kranczioch C. Does Fractional Anisotropy Predict Motor Imagery Neurofeedback Performance in Healthy Older Adults? Front Hum Neurosci 2019; 13:69. [PMID: 30873015 PMCID: PMC6403184 DOI: 10.3389/fnhum.2019.00069] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Accepted: 02/11/2019] [Indexed: 01/02/2023] Open
Abstract
Motor imagery neurofeedback training has been proposed as a potential add-on therapy for motor impairment after stroke, but not everyone benefits from it. Previous work has used white matter integrity to predict motor imagery neurofeedback aptitude in healthy young adults. We set out to test this approach with motor imagery neurofeedback that is closer to that used for stroke rehabilitation and in a sample whose age is closer to that of typical stroke patients. Using shrinkage linear discriminant analysis with fractional anisotropy values in 48 white matter regions as predictors, we predicted whether each participant in a sample of 21 healthy older adults (48–77 years old) was a good or a bad performer with 84.8% accuracy. However, the regions used for prediction in our sample differed from those identified previously, and previously suggested regions did not yield significant prediction in our sample. Including demographic and cognitive variables which may correlate with motor imagery neurofeedback performance and white matter structure as candidate predictors revealed an association with age but also led to loss of statistical significance and somewhat poorer prediction accuracy (69.6%). Our results suggest cast doubt on the feasibility of predicting the benefit of motor imagery neurofeedback from fractional anisotropy. At the very least, such predictions should be based on data collected using the same paradigm and with subjects whose characteristics match those of the target case as closely as possible.
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Affiliation(s)
- Joost Meekes
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany
| | - Stefan Debener
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany.,Cluster of Excellence Hearing4All, University of Oldenburg, Oldenburg, Germany.,Research Center Neurosensory Science, University of Oldenburg, Oldenburg, Germany
| | - Catharina Zich
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany.,Department of Psychiatry, Oxford Centre for Human Brain Activity, Wellcome Center for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom.,Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
| | - Martin G Bleichner
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany.,Cluster of Excellence Hearing4All, University of Oldenburg, Oldenburg, Germany
| | - Cornelia Kranczioch
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany.,Research Center Neurosensory Science, University of Oldenburg, Oldenburg, Germany
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63
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Li C, Chan DCW, Yang X, Ke Y, Yung WH. Prediction of Forelimb Reach Results From Motor Cortex Activities Based on Calcium Imaging and Deep Learning. Front Cell Neurosci 2019; 13:88. [PMID: 30914924 PMCID: PMC6422863 DOI: 10.3389/fncel.2019.00088] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Accepted: 02/20/2019] [Indexed: 12/27/2022] Open
Abstract
Brain-wide activities revealed by neuroimaging and recording techniques have been used to predict motor and cognitive functions in both human and animal models. However, although studies have shown the existence of micrometer-scale spatial organization of neurons in the motor cortex relevant to motor control, two-photon microscopy (TPM) calcium imaging at cellular resolution has not been fully exploited for the same purpose. Here, we ask if calcium imaging data recorded by TPM in rodent brain can provide enough information to predict features of upcoming movement. We collected calcium imaging signal from rostral forelimb area in layer 2/3 of the motor cortex while mice performed a two-dimensional lever reaching task. Images of average calcium activity collected during motion preparation period and inter-trial interval (ITI) were used to predict the forelimb reach results. The evaluation was based on a deep learning model that had been applied for object recognition. We found that the prediction accuracy for both maximum reaching location and trial outcome based on motion preparation period but not ITI were higher than the probabilities governed by chance. Our study demonstrated that imaging data encompassing information on the spatial organization of functional neuronal clusters in the motor cortex is useful in predicting motor acts even in the absence of detailed dynamics of neural activities.
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Affiliation(s)
- Chunyue Li
- School of Biomedical Sciences and Gerald Choa Neuroscience Centre, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Danny C W Chan
- School of Biomedical Sciences and Gerald Choa Neuroscience Centre, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Xiaofeng Yang
- School of Biomedical Sciences and Gerald Choa Neuroscience Centre, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Ya Ke
- School of Biomedical Sciences and Gerald Choa Neuroscience Centre, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Wing-Ho Yung
- School of Biomedical Sciences and Gerald Choa Neuroscience Centre, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong
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64
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Halder S, Leinfelder T, Schulz SM, Kübler A. Neural mechanisms of training an auditory event-related potential task in a brain-computer interface context. Hum Brain Mapp 2019; 40:2399-2412. [PMID: 30693612 DOI: 10.1002/hbm.24531] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 12/18/2018] [Accepted: 01/11/2019] [Indexed: 11/12/2022] Open
Abstract
Effective use of brain-computer interfaces (BCIs) typically requires training. Improved understanding of the neural mechanisms underlying BCI training will facilitate optimisation of BCIs. The current study examined the neural mechanisms related to training for electroencephalography (EEG)-based communication with an auditory event-related potential (ERP) BCI. Neural mechanisms of training in 10 healthy volunteers were assessed with functional magnetic resonance imaging (fMRI) during an auditory ERP-based BCI task before (t1) and after (t5) three ERP-BCI training sessions outside the fMRI scanner (t2, t3, and t4). Attended stimuli were contrasted with ignored stimuli in the first-level fMRI data analysis (t1 and t5); the training effect was verified using the EEG data (t2-t4); and brain activation was contrasted before and after training in the second-level fMRI data analysis (t1 vs. t5). Training increased the communication speed from 2.9 bits/min (t2) to 4 bits/min (t4). Strong activation was found in the putamen, supplementary motor area (SMA), and superior temporal gyrus (STG) associated with attention to the stimuli. Training led to decreased activation in the superior frontal gyrus and stronger haemodynamic rebound in the STG and supramarginal gyrus. The neural mechanisms of ERP-BCI training indicate improved stimulus perception and reduced mental workload. The ERP task used in the current study showed overlapping activations with a motor imagery based BCI task from a previous study on the neural mechanisms of BCI training in the SMA and putamen. This suggests commonalities between the neural mechanisms of training for both BCI paradigms.
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Affiliation(s)
- Sebastian Halder
- School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom.,Institute of Psychology, University of Würzburg, Würzburg, Germany.,Human-Computer Interaction, University of Würzburg, Würzburg, Germany.,Department of Molecular Medicine, University of Oslo, Oslo, Norway
| | | | - Stefan M Schulz
- Institute of Psychology, University of Würzburg, Würzburg, Germany.,Clinical Psychology, Psychotherapy, and Experimental Psychopathology, Johannes Gutenberg University, Mainz, Germany
| | - Andrea Kübler
- Institute of Psychology, University of Würzburg, Würzburg, Germany
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65
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Velasquez-Martinez LF, Luna-Naranjo D, Cárdenas-Peña D, Acosta-Medina C, Castaño GA, Castellanos-Dominguez G. Relevance of Common Spatial Patterns Ranked by Kernel PCA in Motor Imagery Classification. Brain Inform 2019. [DOI: 10.1007/978-3-030-37078-7_2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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66
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Mirror and Vibration Therapies Effects on the Upper Limbs of Hemiparetic Patients after Stroke: A Pilot Study. Rehabil Res Pract 2018; 2018:6183654. [PMID: 30519490 PMCID: PMC6241361 DOI: 10.1155/2018/6183654] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Revised: 07/24/2018] [Accepted: 10/23/2018] [Indexed: 11/18/2022] Open
Abstract
Background/Aim To evaluate, in this pilot study, the effects of the mirror (MT) and vibration therapies (VT) on the functionality of hemiparesis patients after stroke. Materials and Methods Twenty-one individuals after stroke with upper limb hemiparesis were randomized into control group (CG), Mirror Therapy Group (MTG), and Vibration Therapy Group (VTG). The functionality was evaluated before and after 12 sessions with three tests (i) Mobility Index Rivermead, (ii) Motor Function Wolf Test (time, functional ability), and (iii) Jebsen Taylor Test. Results Significant findings were observed for MTG or VTG when compared to the CG, obtaining improvements in the three functional tests: Mobility Index Rivermead, Motor Function Test Wolf (time) and Motor Function Test Wolf (functional ability), and Jebsen Test Taylor. Conclusions MT or VT showed enhancements on the functionality of subjects with poststroke hemiparesis. In consequence, these interventions may be used in the rehabilitation of these individuals in order to promote improvements of the affected upper limb functionality. Probably, neuromuscular responses of the used therapies would be related to these desirable effects. However, it is necessary conducting further controlled studies with more subjects.
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67
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Steyrl D, Müller-Putz GR. Artifacts in EEG of simultaneous EEG-fMRI: pulse artifact remainders in the gradient artifact template are a source of artifact residuals after average artifact subtraction. J Neural Eng 2018; 16:016011. [PMID: 30523809 DOI: 10.1088/1741-2552/aaec42] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
OBJECTIVE The simultaneous application of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) opens up new ways to investigate the human brain. The EEG recordings of simultaneous EEG-fMRI, however, are overlaid to a great degree by fMRI related artifacts and an artifact reduction is mandatory before any EEG analysis. The most severe artifacts-the gradient artifact and the pulse artifact-are repetitive. Average artifact subtraction (AAS) technique exploits the repetitiveness and is presumably the most often used artifact reduction technique. In this method artifact templates are calculated by averaging over adjacent artifact epochs and subsequently the templates are subtracted to reduce the artifacts. Although the AAS technique is one of the best performing methods, artifact residuals are usually present in the resulting EEG after applying the AAS technique. This work aims at identifying sources of the artifact residuals. APPROACH Application of the AAS technique to artificial EEG that is contaminated with artificial fMRI related artifacts. MAIN RESULTS A new source of artifact residuals was identified. It was found that the AAS technique itself adds artifacts to the EEG during gradient artifact reduction, because the gradient artifact template is corrupted by pulse artifact remainders. SIGNIFICANCE This work shows that using a standard number of 25 epochs to calculate the gradient artifact template-as suggested by the inventors of AAS-results in substantial artifact residuals and consequently to a low EEG quality. Furthermore, the work discusses how potential solutions to this problem have serious side effects such as loss of adaptivity of the AAS technique. Hence, this problem must be considered carefully already in the design of simultaneous EEG-fMRI experiments.
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Affiliation(s)
- David Steyrl
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
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68
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Kim HC, Bandettini PA, Lee JH. Deep neural network predicts emotional responses of the human brain from functional magnetic resonance imaging. Neuroimage 2018; 186:607-627. [PMID: 30366076 DOI: 10.1016/j.neuroimage.2018.10.054] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2017] [Revised: 08/15/2018] [Accepted: 10/21/2018] [Indexed: 10/28/2022] Open
Abstract
An artificial neural network with multiple hidden layers (known as a deep neural network, or DNN) was employed as a predictive model (DNNp) for the first time to predict emotional responses using whole-brain functional magnetic resonance imaging (fMRI) data from individual subjects. During fMRI data acquisition, 10 healthy participants listened to 80 International Affective Digital Sound stimuli and rated their own emotions generated by each sound stimulus in terms of the arousal, dominance, and valence dimensions. The whole-brain spatial patterns from a general linear model (i.e., beta-valued maps) for each sound stimulus and the emotional response ratings were used as the input and output for the DNNP, respectively. Based on a nested five-fold cross-validation scheme, the paired input and output data were divided into training (three-fold), validation (one-fold), and test (one-fold) data. The DNNP was trained and optimized using the training and validation data and was tested using the test data. The Pearson's correlation coefficients between the rated and predicted emotional responses from our DNNP model with weight sparsity optimization (mean ± standard error 0.52 ± 0.02 for arousal, 0.51 ± 0.03 for dominance, and 0.51 ± 0.03 for valence, with an input denoising level of 0.3 and a mini-batch size of 1) were significantly greater than those of DNN models with conventional regularization schemes including elastic net regularization (0.15 ± 0.05, 0.15 ± 0.06, and 0.21 ± 0.04 for arousal, dominance, and valence, respectively), those of shallow models including logistic regression (0.11 ± 0.04, 0.10 ± 0.05, and 0.17 ± 0.04 for arousal, dominance, and valence, respectively; average of logistic regression and sparse logistic regression), and those of support vector machine-based predictive models (SVMps; 0.12 ± 0.06, 0.06 ± 0.06, and 0.10 ± 0.06 for arousal, dominance, and valence, respectively; average of linear and non-linear SVMps). This difference was confirmed to be significant with a Bonferroni-corrected p-value of less than 0.001 from a one-way analysis of variance (ANOVA) and subsequent paired t-test. The weights of the trained DNNPs were interpreted and input patterns that maximized or minimized the output of the DNNPs (i.e., the emotional responses) were estimated. Based on a binary classification of each emotion category (e.g., high arousal vs. low arousal), the error rates for the DNNP (31.2% ± 1.3% for arousal, 29.0% ± 1.7% for dominance, and 28.6% ± 3.0% for valence) were significantly lower than those for the linear SVMP (44.7% ± 2.0%, 50.7% ± 1.7%, and 47.4% ± 1.9% for arousal, dominance, and valence, respectively) and the non-linear SVMP (48.8% ± 2.3%, 52.2% ± 1.9%, and 46.4% ± 1.3% for arousal, dominance, and valence, respectively), as confirmed by the Bonferroni-corrected p < 0.001 from the one-way ANOVA. Our study demonstrates that the DNNp model is able to reveal neuronal circuitry associated with human emotional processing - including structures in the limbic and paralimbic areas, which include the amygdala, prefrontal areas, anterior cingulate cortex, insula, and caudate. Our DNNp model was also able to use activation patterns in these structures to predict and classify emotional responses to stimuli.
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Affiliation(s)
- Hyun-Chul Kim
- Department of Brain and Cognitive Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Peter A Bandettini
- Section on Functional Imaging Methods, Lab of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Jong-Hwan Lee
- Department of Brain and Cognitive Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul, 02841, Republic of Korea.
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69
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Petrantonakis PC, Kompatsiaris I. Single-Trial NIRS Data Classification for Brain–Computer Interfaces Using Graph Signal Processing. IEEE Trans Neural Syst Rehabil Eng 2018; 26:1700-1709. [DOI: 10.1109/tnsre.2018.2860629] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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70
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Toriyama H, Ushiba J, Ushiyama J. Subjective Vividness of Kinesthetic Motor Imagery Is Associated With the Similarity in Magnitude of Sensorimotor Event-Related Desynchronization Between Motor Execution and Motor Imagery. Front Hum Neurosci 2018; 12:295. [PMID: 30108492 PMCID: PMC6079198 DOI: 10.3389/fnhum.2018.00295] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Accepted: 07/05/2018] [Indexed: 11/26/2022] Open
Abstract
In the field of psychology, it has been well established that there are two types of motor imagery such as kinesthetic motor imagery (KMI) and visual motor imagery (VMI), and the subjective evaluation for vividness of motor imagery each differs across individuals. This study aimed to examine how the motor imagery ability assessed by the psychological scores is associated with the physiological measure using electroencephalogram (EEG) sensorimotor rhythm during KMI task. First, 20 healthy young individuals evaluated subjectively how vividly they can perform each of KMI and VMI by using the Kinesthetic and Visual Imagery Questionnaire (KVIQ). We assessed their motor imagery abilities by summing each of KMI and VMI scores in KVIQ (KMItotal and VMItotal). Second, in physiological experiments, they repeated two strengths (10 and 40% of maximal effort) of isometric voluntary wrist-dorsiflexion. Right after each contraction, they also performed its KMI. The scalp EEGs over the sensorimotor cortex were recorded during the tasks. The EEG power is known to decrease in the alpha-and-beta band (7–35 Hz) from resting state to performing state of voluntary contraction (VC) or motor imagery. This phenomenon is referred to as event-related desynchronization (ERD). For each strength of the tasks, we calculated the maximal peak of ERD during VC, and that during its KMI, and measured the degree of similarity (ERDsim) between them. The results showed significant negative correlations between KMItotal and ERDsim for both strengths (p < 0.05) (i.e., the higher the KMItotal, the smaller the ERDsim). These findings suggest that in healthy individuals with higher motor imagery ability from a first-person perspective, KMI efficiently engages the shared cortical circuits corresponding with motor execution, including the sensorimotor cortex, with high compliance.
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Affiliation(s)
- Hisato Toriyama
- Graduate School of Media and Governance, Keio University, Fujisawa, Japan
| | - Junichi Ushiba
- Department of Biosciences and Informatics, Faculty of Science and Technology, Keio University, Yokohama, Japan.,Keio Institute of Pure and Applied Sciences, Yokohama, Japan
| | - Junichi Ushiyama
- Faculty of Environment and Information Studies, Keio University, Fujisawa, Japan.,Department of Rehabilitation Medicine, Keio University School of Medicine, Keio University, Tokyo, Japan
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71
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Adaptive optimal basis set for BCG artifact removal in simultaneous EEG-fMRI. Sci Rep 2018; 8:8902. [PMID: 29891929 PMCID: PMC5995808 DOI: 10.1038/s41598-018-27187-6] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Accepted: 05/30/2018] [Indexed: 11/13/2022] Open
Abstract
Electroencephalography (EEG) signals recorded during simultaneous functional magnetic resonance imaging (fMRI) are contaminated by strong artifacts. Among these, the ballistocardiographic (BCG) artifact is the most challenging, due to its complex spatio-temporal dynamics associated with ongoing cardiac activity. The presence of BCG residuals in EEG data may hide true, or generate spurious correlations between EEG and fMRI time-courses. Here, we propose an adaptive Optimal Basis Set (aOBS) method for BCG artifact removal. Our method is adaptive, as it can estimate the delay between cardiac activity and BCG occurrence on a beat-to-beat basis. The effective creation of an optimal basis set by principal component analysis (PCA) is therefore ensured by a more accurate alignment of BCG occurrences. Furthermore, aOBS can automatically estimate which components produced by PCA are likely to be BCG artifact-related and therefore need to be removed. The aOBS performance was evaluated on high-density EEG data acquired with simultaneous fMRI in healthy subjects during visual stimulation. As aOBS enables effective reduction of BCG residuals while preserving brain signals, we suggest it may find wide application in simultaneous EEG-fMRI studies.
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72
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Lotte F, Jeunet C. Defining and quantifying users' mental imagery-based BCI skills: a first step. J Neural Eng 2018; 15:046030. [PMID: 29769435 DOI: 10.1088/1741-2552/aac577] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE While promising for many applications, electroencephalography (EEG)-based brain-computer interfaces (BCIs) are still scarcely used outside laboratories, due to a poor reliability. It is thus necessary to study and fix this reliability issue. Doing so requires the use of appropriate reliability metrics to quantify both the classification algorithm and the BCI user's performances. So far, classification accuracy (CA) is the typical metric used for both aspects. However, we argue in this paper that CA is a poor metric to study BCI users' skills. Here, we propose a definition and new metrics to quantify such BCI skills for mental imagery (MI) BCIs, independently of any classification algorithm. APPROACH We first show in this paper that CA is notably unspecific, discrete, training data and classifier dependent, and as such may not always reflect successful self-modulation of EEG patterns by the user. We then propose a definition of MI-BCI skills that reflects how well the user can self-modulate EEG patterns, and thus how well he could control an MI-BCI. Finally, we propose new performance metrics, classDis, restDist and classStab that specifically measure how distinct and stable the EEG patterns produced by the user are, independently of any classifier. MAIN RESULTS By re-analyzing EEG data sets with such new metrics, we indeed confirmed that CA may hide some increase in MI-BCI skills or hide the user inability to self-modulate a given EEG pattern. On the other hand, our new metrics could reveal such skill improvements as well as identify when a mental task performed by a user was no different than rest EEG. SIGNIFICANCE Our results showed that when studying MI-BCI users' skills, CA should be used with care, and complemented with metrics such as the new ones proposed. Our results also stressed the need to redefine BCI user training by considering the different BCI subskills and their measures. To promote the complementary use of our new metrics, we provide the Matlab code to compute them for free and open-source.
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Affiliation(s)
- Fabien Lotte
- Inria Bordeaux Sud-Ouest, Talence, France. LaBRI-CNRS/University of Bordeaux/INP Bordeaux, Talence, France
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73
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Bagarinao E, Yoshida A, Ueno M, Terabe K, Kato S, Isoda H, Nakai T. Improved Volitional Recall of Motor-Imagery-Related Brain Activation Patterns Using Real-Time Functional MRI-Based Neurofeedback. Front Hum Neurosci 2018; 12:158. [PMID: 29740302 PMCID: PMC5928248 DOI: 10.3389/fnhum.2018.00158] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2017] [Accepted: 04/05/2018] [Indexed: 11/13/2022] Open
Abstract
Motor imagery (MI), a covert cognitive process where an action is mentally simulated but not actually performed, could be used as an effective neurorehabilitation tool for motor function improvement or recovery. Recent approaches employing brain–computer/brain–machine interfaces to provide online feedback of the MI during rehabilitation training have promising rehabilitation outcomes. In this study, we examined whether participants could volitionally recall MI-related brain activation patterns when guided using neurofeedback (NF) during training. The participants’ performance was compared to that without NF. We hypothesized that participants would be able to consistently generate the relevant activation pattern associated with the MI task during training with NF compared to that without NF. To assess activation consistency, we used the performance of classifiers trained to discriminate MI-related brain activation patterns. Our results showed significantly higher predictive values of MI-related activation patterns during training with NF. Additionally, this improvement in the classification performance tends to be associated with the activation of middle temporal gyrus/inferior occipital gyrus, a region associated with visual motion processing, suggesting the importance of performance monitoring during MI task training. Taken together, these findings suggest that the efficacy of MI training, in terms of generating consistent brain activation patterns relevant to the task, can be enhanced by using NF as a mechanism to enable participants to volitionally recall task-related brain activation patterns.
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Affiliation(s)
| | - Akihiro Yoshida
- Department of Radiological Sciences, Nagoya University Graduate School of Medicine, Nagoya University, Nagoya, Japan.,NeuroImaging and Informatics Lab, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Mika Ueno
- NeuroImaging and Informatics Lab, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Kazunori Terabe
- Graduate School of Engineering, Nagoya Institute of Technology, Nagoya, Japan
| | - Shohei Kato
- Graduate School of Engineering, Nagoya Institute of Technology, Nagoya, Japan
| | - Haruo Isoda
- Brain & Mind Research Center, Nagoya University, Nagoya, Japan.,Department of Radiological Sciences, Nagoya University Graduate School of Medicine, Nagoya University, Nagoya, Japan
| | - Toshiharu Nakai
- Department of Radiological Sciences, Nagoya University Graduate School of Medicine, Nagoya University, Nagoya, Japan.,NeuroImaging and Informatics Lab, National Center for Geriatrics and Gerontology, Obu, Japan
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74
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Rodriguez-Ugarte MDLS, Iáñez E, Ortiz-Garcia M, Azorín JM. Effects of tDCS on Real-Time BCI Detection of Pedaling Motor Imagery. SENSORS (BASEL, SWITZERLAND) 2018; 18:E1136. [PMID: 29642493 PMCID: PMC5948891 DOI: 10.3390/s18041136] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 03/15/2018] [Accepted: 04/05/2018] [Indexed: 12/04/2022]
Abstract
The purpose of this work is to strengthen the cortical excitability over the primary motor cortex (M1) and the cerebro-cerebellar pathway by means of a new transcranial direct current stimulation (tDCS) configuration to detect lower limb motor imagery (MI) in real time using two different cognitive neural states: relax and pedaling MI. The anode is located over the primary motor cortex in Cz, and the cathode over the right cerebro-cerebellum. The real-time brain-computer interface (BCI) designed is based on finding, for each electrode selected, the power at the particular frequency where the most difference between the two mental tasks is observed. Electroencephalographic (EEG) electrodes are placed over the brain's premotor area (PM), M1, supplementary motor area (SMA) and primary somatosensory cortex (S1). A single-blind study is carried out, where fourteen healthy subjects are separated into two groups: sham and active tDCS. Each subject is experimented on for five consecutive days. On all days, the results achieved by the active tDCS group were over 60% in real-time detection accuracy, with a five-day average of 62.6%. The sham group eventually reached those levels of accuracy, but it needed three days of training to do so.
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Affiliation(s)
- Maria de la Soledad Rodriguez-Ugarte
- Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, Avda. de la Universidad S/N Ed. Innova, Elche, 03202 Alicante, Spain.
| | - Eduardo Iáñez
- Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, Avda. de la Universidad S/N Ed. Innova, Elche, 03202 Alicante, Spain.
| | - Mario Ortiz-Garcia
- Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, Avda. de la Universidad S/N Ed. Innova, Elche, 03202 Alicante, Spain
| | - José M Azorín
- Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, Avda. de la Universidad S/N Ed. Innova, Elche, 03202 Alicante, Spain.
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75
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Meng J, Edelman BJ, Olsoe J, Jacobs G, Zhang S, Beyko A, He B. A Study of the Effects of Electrode Number and Decoding Algorithm on Online EEG-Based BCI Behavioral Performance. Front Neurosci 2018; 12:227. [PMID: 29681792 PMCID: PMC5897442 DOI: 10.3389/fnins.2018.00227] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Accepted: 03/22/2018] [Indexed: 11/25/2022] Open
Abstract
Motor imagery–based brain–computer interface (BCI) using electroencephalography (EEG) has demonstrated promising applications by directly decoding users' movement related mental intention. The selection of control signals, e.g., the channel configuration and decoding algorithm, plays a vital role in the online performance and progressing of BCI control. While several offline analyses report the effect of these factors on BCI accuracy for a single session—performance increases asymptotically by increasing the number of channels, saturates, and then decreases—no online study, to the best of our knowledge, has yet been performed to compare for a single session or across training. The purpose of the current study is to assess, in a group of forty-five subjects, the effect of channel number and decoding method on the progression of BCI performance across multiple training sessions and the corresponding neurophysiological changes. The 45 subjects were divided into three groups using Laplacian Filtering (LAP/S) with nine channels, Common Spatial Pattern (CSP/L) with 40 channels and CSP (CSP/S) with nine channels for online decoding. At the first training session, subjects using CSP/L displayed no significant difference compared to CSP/S but a higher average BCI performance over those using LAP/S. Despite the average performance when using the LAP/S method was initially lower, but LAP/S displayed improvement over first three sessions, whereas the other two groups did not. Additionally, analysis of the recorded EEG during BCI control indicates that the LAP/S produces control signals that are more strongly correlated with the target location and a higher R-square value was shown at the fifth session. In the present study, we found that subjects' average online BCI performance using a large EEG montage does not show significantly better performance after the first session than a smaller montage comprised of a common subset of these electrodes. The LAP/S method with a small EEG montage allowed the subjects to improve their skills across sessions, but no improvement was shown for the CSP method.
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Affiliation(s)
- Jianjun Meng
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Bradley J Edelman
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Jaron Olsoe
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Gabriel Jacobs
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Shuying Zhang
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Angeliki Beyko
- Institute for Engineering in Medicine, University of Minnesota, Minneapolis, MN, United States
| | - Bin He
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States
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76
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Saha S, Ahmed KIU, Mostafa R, Hadjileontiadis L, Khandoker A. Evidence of Variabilities in EEG Dynamics During Motor Imagery-Based Multiclass Brain–Computer Interface. IEEE Trans Neural Syst Rehabil Eng 2018; 26:371-382. [DOI: 10.1109/tnsre.2017.2778178] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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77
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Lazarou I, Nikolopoulos S, Petrantonakis PC, Kompatsiaris I, Tsolaki M. EEG-Based Brain-Computer Interfaces for Communication and Rehabilitation of People with Motor Impairment: A Novel Approach of the 21 st Century. Front Hum Neurosci 2018; 12:14. [PMID: 29472849 PMCID: PMC5810272 DOI: 10.3389/fnhum.2018.00014] [Citation(s) in RCA: 110] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Accepted: 01/12/2018] [Indexed: 12/14/2022] Open
Abstract
People with severe neurological impairments face many challenges in sensorimotor functions and communication with the environment; therefore they have increased demand for advanced, adaptive and personalized rehabilitation. During the last several decades, numerous studies have developed brain-computer interfaces (BCIs) with the goals ranging from providing means of communication to functional rehabilitation. Here we review the research on non-invasive, electroencephalography (EEG)-based BCI systems for communication and rehabilitation. We focus on the approaches intended to help severely paralyzed and locked-in patients regain communication using three different BCI modalities: slow cortical potentials, sensorimotor rhythms and P300 potentials, as operational mechanisms. We also review BCI systems for restoration of motor function in patients with spinal cord injury and chronic stroke. We discuss the advantages and limitations of these approaches and the challenges that need to be addressed in the future.
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Affiliation(s)
- Ioulietta Lazarou
- Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece.,1st Department of Neurology, University Hospital "AHEPA", School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.,Greek Association of Alzheimer's Disease and Related Disorders, Thessaloniki, Greece
| | - Spiros Nikolopoulos
- Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece
| | | | - Ioannis Kompatsiaris
- Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece
| | - Magda Tsolaki
- Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece.,1st Department of Neurology, University Hospital "AHEPA", School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.,Greek Association of Alzheimer's Disease and Related Disorders, Thessaloniki, Greece
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78
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Steyrl D, Krausz G, Koschutnig K, Edlinger G, Müller-Putz GR. Online Reduction of Artifacts in EEG of Simultaneous EEG-fMRI Using Reference Layer Adaptive Filtering (RLAF). Brain Topogr 2018; 31:129-149. [PMID: 29124547 PMCID: PMC5772120 DOI: 10.1007/s10548-017-0606-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2017] [Accepted: 10/31/2017] [Indexed: 11/29/2022]
Abstract
Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow us to study the active human brain from two perspectives concurrently. Signal processing based artifact reduction techniques are mandatory for this, however, to obtain reasonable EEG quality in simultaneous EEG-fMRI. Current artifact reduction techniques like average artifact subtraction (AAS), typically become less effective when artifact reduction has to be performed on-the-fly. We thus present and evaluate a new technique to improve EEG quality online. This technique adds up with online AAS and combines a prototype EEG-cap for reference recordings of artifacts, with online adaptive filtering and is named reference layer adaptive filtering (RLAF). We found online AAS + RLAF to be highly effective in improving EEG quality. Online AAS + RLAF outperformed online AAS and did so in particular online in terms of the chosen performance metrics, these being specifically alpha rhythm amplitude ratio between closed and opened eyes (3-45% improvement), signal-to-noise-ratio of visual evoked potentials (VEP) (25-63% improvement), and VEPs variability (16-44% improvement). Further, we found that EEG quality after online AAS + RLAF is occasionally even comparable with the offline variant of AAS at a 3T MRI scanner. In conclusion RLAF is a very effective add-on tool to enable high quality EEG in simultaneous EEG-fMRI experiments, even when online artifact reduction is necessary.
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Affiliation(s)
- David Steyrl
- Laboratory of Brain-Computer Interfaces, Institute of Neural Engineering, Graz University of Technology, 8010, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | | | - Karl Koschutnig
- Department of Psychology, University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | | | - Gernot R Müller-Putz
- Laboratory of Brain-Computer Interfaces, Institute of Neural Engineering, Graz University of Technology, 8010, Graz, Austria.
- BioTechMed-Graz, Graz, Austria.
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79
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Zich C, Debener S, Schweinitz C, Sterr A, Meekes J, Kranczioch C. High-Intensity Chronic Stroke Motor Imagery Neurofeedback Training at Home: Three Case Reports. Clin EEG Neurosci 2017; 48:403-412. [PMID: 28677413 DOI: 10.1177/1550059417717398] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Motor imagery (MI) with neurofeedback has been suggested as promising for motor recovery after stroke. Evidence suggests that regular training facilitates compensatory plasticity, but frequent training is difficult to integrate into everyday life. Using a wireless electroencephalogram (EEG) system, we implemented a frequent and efficient neurofeedback training at the patients' home. Aiming to overcome maladaptive changes in cortical lateralization patterns we presented a visual feedback, representing the degree of contralateral sensorimotor cortical activity and the degree of sensorimotor cortex lateralization. Three stroke patients practiced every other day, over a period of 4 weeks. Training-related changes were evaluated on behavioral, functional, and structural levels. All 3 patients indicated that they enjoyed the training and were highly motivated throughout the entire training regime. EEG activity induced by MI of the affected hand became more lateralized over the course of training in all three patients. The patient with a significant functional change also showed increased white matter integrity as revealed by diffusion tensor imaging, and a substantial clinical improvement of upper limb motor functions. Our study provides evidence that regular, home-based practice of MI neurofeedback has the potential to facilitate cortical reorganization and may also increase associated improvements of upper limb motor function in chronic stroke patients.
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Affiliation(s)
- Catharina Zich
- 1 Neuropsychology Lab, Department of Psychology, European Medical School, University of Oldenburg, Oldenburg, Germany.,2 Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Stefan Debener
- 1 Neuropsychology Lab, Department of Psychology, European Medical School, University of Oldenburg, Oldenburg, Germany.,3 Cluster of Excellence Hearing4all, University of Oldenburg, Oldenburg, Germany.,4 Research Center Neurosensory Systems, University of Oldenburg, Oldenburg, Germany
| | - Clara Schweinitz
- 1 Neuropsychology Lab, Department of Psychology, European Medical School, University of Oldenburg, Oldenburg, Germany
| | - Annette Sterr
- 5 Brain and Behaviour Research Group, School of Psychology, University of Surrey, Guildford, UK
| | - Joost Meekes
- 1 Neuropsychology Lab, Department of Psychology, European Medical School, University of Oldenburg, Oldenburg, Germany
| | - Cornelia Kranczioch
- 1 Neuropsychology Lab, Department of Psychology, European Medical School, University of Oldenburg, Oldenburg, Germany.,4 Research Center Neurosensory Systems, University of Oldenburg, Oldenburg, Germany
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80
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Zich C, Harty S, Kranczioch C, Mansfield KL, Sella F, Debener S, Cohen Kadosh R. Modulating hemispheric lateralization by brain stimulation yields gain in mental and physical activity. Sci Rep 2017; 7:13430. [PMID: 29044223 PMCID: PMC5647441 DOI: 10.1038/s41598-017-13795-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Accepted: 10/02/2017] [Indexed: 01/24/2023] Open
Abstract
Imagery plays an important role in our life. Motor imagery is the mental simulation of a motor act without overt motor output. Previous studies have documented the effect of motor imagery practice. However, its translational potential for patients as well as for athletes, musicians and other groups, depends largely on the transfer from mental practice to overt physical performance. We used bilateral transcranial direct current stimulation (tDCS) over sensorimotor areas to modulate neural lateralization patterns induced by unilateral mental motor imagery and the performance of a physical motor task. Twenty-six healthy older adults participated (mean age = 67.1 years) in a double-blind cross-over sham-controlled study. We found stimulation-related changes at the neural and behavioural level, which were polarity-dependent. Specifically, for the hand contralateral to the anode, electroencephalographic activity induced by motor imagery was more lateralized and motor performance improved. In contrast, for the hand contralateral to the cathode, hemispheric lateralization was reduced. The stimulation-related increase and decrease in neural lateralization were negatively related. Further, the degree of stimulation-related change in neural lateralization correlated with the stimulation-related change on behavioural level. These convergent neurophysiological and behavioural effects underline the potential of tDCS to improve mental and physical motor performance.
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Affiliation(s)
- Catharina Zich
- Department of Psychology, University of Oldenburg, 26111, Oldenburg, Germany. .,Department of Experimental Psychology, University of Oxford, OX1 3UD, Oxford, UK.
| | - Siobhán Harty
- Department of Experimental Psychology, University of Oxford, OX1 3UD, Oxford, UK
| | - Cornelia Kranczioch
- Department of Psychology, University of Oldenburg, 26111, Oldenburg, Germany
| | - Karen L Mansfield
- Department of Experimental Psychology, University of Oxford, OX1 3UD, Oxford, UK
| | - Francesco Sella
- Department of Experimental Psychology, University of Oxford, OX1 3UD, Oxford, UK
| | - Stefan Debener
- Department of Psychology, University of Oldenburg, 26111, Oldenburg, Germany
| | - Roi Cohen Kadosh
- Department of Experimental Psychology, University of Oxford, OX1 3UD, Oxford, UK.
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81
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P 69 Assessing the relation between brain structure and function during motor imagery in stroke patients and controls using EEG and MRI. Clin Neurophysiol 2017. [DOI: 10.1016/j.clinph.2017.06.145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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82
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Koush Y, Ashburner J, Prilepin E, Sladky R, Zeidman P, Bibikov S, Scharnowski F, Nikonorov A, De Ville DV. OpenNFT: An open-source Python/Matlab framework for real-time fMRI neurofeedback training based on activity, connectivity and multivariate pattern analysis. Neuroimage 2017. [PMID: 28645842 DOI: 10.1016/j.neuroimage.2017.06.039] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Neurofeedback based on real-time functional magnetic resonance imaging (rt-fMRI) is a novel and rapidly developing research field. It allows for training of voluntary control over localized brain activity and connectivity and has demonstrated promising clinical applications. Because of the rapid technical developments of MRI techniques and the availability of high-performance computing, new methodological advances in rt-fMRI neurofeedback become possible. Here we outline the core components of a novel open-source neurofeedback framework, termed Open NeuroFeedback Training (OpenNFT), which efficiently integrates these new developments. This framework is implemented using Python and Matlab source code to allow for diverse functionality, high modularity, and rapid extendibility of the software depending on the user's needs. In addition, it provides an easy interface to the functionality of Statistical Parametric Mapping (SPM) that is also open-source and one of the most widely used fMRI data analysis software. We demonstrate the functionality of our new framework by describing case studies that include neurofeedback protocols based on brain activity levels, effective connectivity models, and pattern classification approaches. This open-source initiative provides a suitable framework to actively engage in the development of novel neurofeedback approaches, so that local methodological developments can be easily made accessible to a wider range of users.
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Affiliation(s)
- Yury Koush
- Department of Radiology and Medical Imaging, Yale University, New Haven, USA; Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
| | - John Ashburner
- Wellcome Trust Centre for Neuroimaging, University College London, London, UK
| | - Evgeny Prilepin
- Aligned Research Group, 20 S Santa Cruz Ave 300, 95030 Los Gatos, CA, USA
| | - Ronald Sladky
- Department of Psychiatric, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zürich, Lenggstrasse 31, 8032 Zürich, Switzerland; Neuroscience Center Zürich, University of Zürich and Swiss Federal Institute of Technology, Winterthurerstr. 190, 8057 Zürich, Switzerland; Zürich Center for Integrative Human Physiology (ZIHP), University of Zürich, Winterthurerstr. 190, 8057 Zürich, Switzerland
| | - Peter Zeidman
- Wellcome Trust Centre for Neuroimaging, University College London, London, UK
| | - Sergei Bibikov
- Supercomputers and Computer Science Department, Samara University, Moskovskoe shosse str., 34, 443086 Samara, Russia; Image Processing Systems Institute of Russian Academy of Science, Molodogvardeyskaya str., 151, 443001 Samara, Russia
| | - Frank Scharnowski
- Department of Psychiatric, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zürich, Lenggstrasse 31, 8032 Zürich, Switzerland; Neuroscience Center Zürich, University of Zürich and Swiss Federal Institute of Technology, Winterthurerstr. 190, 8057 Zürich, Switzerland; Zürich Center for Integrative Human Physiology (ZIHP), University of Zürich, Winterthurerstr. 190, 8057 Zürich, Switzerland
| | - Artem Nikonorov
- Aligned Research Group, 20 S Santa Cruz Ave 300, 95030 Los Gatos, CA, USA; Supercomputers and Computer Science Department, Samara University, Moskovskoe shosse str., 34, 443086 Samara, Russia; Image Processing Systems Institute of Russian Academy of Science, Molodogvardeyskaya str., 151, 443001 Samara, Russia
| | - Dimitri Van De Ville
- Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
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83
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Perronnet L, Lécuyer A, Mano M, Bannier E, Lotte F, Clerc M, Barillot C. Unimodal Versus Bimodal EEG-fMRI Neurofeedback of a Motor Imagery Task. Front Hum Neurosci 2017; 11:193. [PMID: 28473762 PMCID: PMC5397479 DOI: 10.3389/fnhum.2017.00193] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Accepted: 04/03/2017] [Indexed: 11/30/2022] Open
Abstract
Neurofeedback is a promising tool for brain rehabilitation and peak performance training. Neurofeedback approaches usually rely on a single brain imaging modality such as EEG or fMRI. Combining these modalities for neurofeedback training could allow to provide richer information to the subject and could thus enable him/her to achieve faster and more specific self-regulation. Yet unimodal and multimodal neurofeedback have never been compared before. In the present work, we introduce a simultaneous EEG-fMRI experimental protocol in which participants performed a motor-imagery task in unimodal and bimodal NF conditions. With this protocol we were able to compare for the first time the effects of unimodal EEG-neurofeedback and fMRI-neurofeedback versus bimodal EEG-fMRI-neurofeedback by looking both at EEG and fMRI activations. We also propose a new feedback metaphor for bimodal EEG-fMRI-neurofeedback that integrates both EEG and fMRI signal in a single bi-dimensional feedback (a ball moving in 2D). Such a feedback is intended to relieve the cognitive load of the subject by presenting the bimodal neurofeedback task as a single regulation task instead of two. Additionally, this integrated feedback metaphor gives flexibility on defining a bimodal neurofeedback target. Participants were able to regulate activity in their motor regions in all NF conditions. Moreover, motor activations as revealed by offline fMRI analysis were stronger during EEG-fMRI-neurofeedback than during EEG-neurofeedback. This result suggests that EEG-fMRI-neurofeedback could be more specific or more engaging than EEG-neurofeedback. Our results also suggest that during EEG-fMRI-neurofeedback, participants tended to regulate more the modality that was harder to control. Taken together our results shed first light on the specific mechanisms of bimodal EEG-fMRI-neurofeedback and on its added-value as compared to unimodal EEG-neurofeedback and fMRI-neurofeedback.
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Affiliation(s)
- Lorraine Perronnet
- INRIA, VisAGeS Project TeamRennes, France.,Centre National de la Recherche Scientifique, IRISA, UMR 6074Rennes, France.,Institut National de la Santé et de la Recherche Médicale, U1228Rennes, France.,Université Rennes 1Rennes, France.,INRIA, Hybrid Project TeamRennes, France
| | - Anatole Lécuyer
- Centre National de la Recherche Scientifique, IRISA, UMR 6074Rennes, France.,INRIA, Hybrid Project TeamRennes, France
| | - Marsel Mano
- INRIA, VisAGeS Project TeamRennes, France.,Centre National de la Recherche Scientifique, IRISA, UMR 6074Rennes, France.,Institut National de la Santé et de la Recherche Médicale, U1228Rennes, France.,Université Rennes 1Rennes, France.,INRIA, Hybrid Project TeamRennes, France
| | - Elise Bannier
- INRIA, VisAGeS Project TeamRennes, France.,Centre National de la Recherche Scientifique, IRISA, UMR 6074Rennes, France.,Institut National de la Santé et de la Recherche Médicale, U1228Rennes, France.,Université Rennes 1Rennes, France.,CHU RennesRennes, France
| | - Fabien Lotte
- Inria, Potioc Project TeamTalence, France.,LaBRIBordeaux, France
| | - Maureen Clerc
- Inria, Athena Project TeamSophia Antipolis, France.,Université Côte d'AzurNice, France
| | - Christian Barillot
- INRIA, VisAGeS Project TeamRennes, France.,Centre National de la Recherche Scientifique, IRISA, UMR 6074Rennes, France.,Institut National de la Santé et de la Recherche Médicale, U1228Rennes, France.,Université Rennes 1Rennes, France
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84
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Braun N, Kranczioch C, Liepert J, Dettmers C, Zich C, Büsching I, Debener S. Motor Imagery Impairment in Postacute Stroke Patients. Neural Plast 2017; 2017:4653256. [PMID: 28458926 PMCID: PMC5387846 DOI: 10.1155/2017/4653256] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2016] [Accepted: 02/14/2017] [Indexed: 01/26/2023] Open
Abstract
Not much is known about how well stroke patients are able to perform motor imagery (MI) and which MI abilities are preserved after stroke. We therefore applied three different MI tasks (one mental chronometry task, one mental rotation task, and one EEG-based neurofeedback task) to a sample of postacute stroke patients (n = 20) and age-matched healthy controls (n = 20) for addressing the following questions: First, which of the MI tasks indicate impairment in stroke patients and are impairments restricted to the paretic side? Second, is there a relationship between MI impairment and sensory loss or paresis severity? And third, do the results of the different MI tasks converge? Significant differences between the stroke and control groups were found in all three MI tasks. However, only the mental chronometry task and EEG analysis revealed paresis side-specific effects. Moreover, sensitivity loss contributed to a performance drop in the mental rotation task. The findings indicate that although MI abilities may be impaired after stroke, most patients retain their ability for MI EEG-based neurofeedback. Interestingly, performance in the different MI measures did not strongly correlate, neither in stroke patients nor in healthy controls. We conclude that one MI measure is not sufficient to fully assess an individual's MI abilities.
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Affiliation(s)
- Niclas Braun
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany
| | - Cornelia Kranczioch
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany
| | | | | | - Catharina Zich
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany
| | | | - Stefan Debener
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany
- Research Center Neurosensory Science, University of Oldenburg, Oldenburg, Germany
- Cluster of Excellence Hearing4All, University of Oldenburg, Oldenburg, Germany
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85
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Mano M, Lécuyer A, Bannier E, Perronnet L, Noorzadeh S, Barillot C. How to Build a Hybrid Neurofeedback Platform Combining EEG and fMRI. Front Neurosci 2017; 11:140. [PMID: 28377691 PMCID: PMC5359276 DOI: 10.3389/fnins.2017.00140] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2016] [Accepted: 03/07/2017] [Indexed: 01/18/2023] Open
Abstract
Multimodal neurofeedback estimates brain activity using information acquired with more than one neurosignal measurement technology. In this paper we describe how to set up and use a hybrid platform based on simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), then we illustrate how to use it for conducting bimodal neurofeedback experiments. The paper is intended for those willing to build a multimodal neurofeedback system, to guide them through the different steps of the design, setup, and experimental applications, and help them choose a suitable hardware and software configuration. Furthermore, it reports practical information from bimodal neurofeedback experiments conducted in our lab. The platform presented here has a modular parallel processing architecture that promotes real-time signal processing performance and simple future addition and/or replacement of processing modules. Various unimodal and bimodal neurofeedback experiments conducted in our lab showed high performance and accuracy. Currently, the platform is able to provide neurofeedback based on electroencephalography and functional magnetic resonance imaging, but the architecture and the working principles described here are valid for any other combination of two or more real-time brain activity measurement technologies.
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Affiliation(s)
- Marsel Mano
- Institut National de Recherche en Informatique et en Automatique (INRIA) Rennes, France
| | - Anatole Lécuyer
- Institut National de Recherche en Informatique et en Automatique (INRIA)Rennes, France; Institut de Recherche en Informatique et Systèmes Aléatoires (IIRISA)Rennes, France
| | - Elise Bannier
- Institut de Recherche en Informatique et Systèmes Aléatoires (IIRISA)Rennes, France; CHU PontchaillouRennes, France
| | - Lorraine Perronnet
- Institut National de Recherche en Informatique et en Automatique (INRIA) Rennes, France
| | - Saman Noorzadeh
- Institut National de Recherche en Informatique et en Automatique (INRIA) Rennes, France
| | - Christian Barillot
- Institut National de Recherche en Informatique et en Automatique (INRIA)Rennes, France; Institut de Recherche en Informatique et Systèmes Aléatoires (IIRISA)Rennes, France; Institut National de la Santé et de la Recherche MédicaleRennes, France
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86
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Marchesotti S, Martuzzi R, Schurger A, Blefari ML, Del Millán JR, Bleuler H, Blanke O. Cortical and subcortical mechanisms of brain-machine interfaces. Hum Brain Mapp 2017; 38:2971-2989. [PMID: 28321973 DOI: 10.1002/hbm.23566] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Revised: 02/28/2017] [Accepted: 03/03/2017] [Indexed: 01/06/2023] Open
Abstract
Technical advances in the field of Brain-Machine Interfaces (BMIs) enable users to control a variety of external devices such as robotic arms, wheelchairs, virtual entities and communication systems through the decoding of brain signals in real time. Most BMI systems sample activity from restricted brain regions, typically the motor and premotor cortex, with limited spatial resolution. Despite the growing number of applications, the cortical and subcortical systems involved in BMI control are currently unknown at the whole-brain level. Here, we provide a comprehensive and detailed report of the areas active during on-line BMI control. We recorded functional magnetic resonance imaging (fMRI) data while participants controlled an EEG-based BMI inside the scanner. We identified the regions activated during BMI control and how they overlap with those involved in motor imagery (without any BMI control). In addition, we investigated which regions reflect the subjective sense of controlling a BMI, the sense of agency for BMI-actions. Our data revealed an extended cortical-subcortical network involved in operating a motor-imagery BMI. This includes not only sensorimotor regions but also the posterior parietal cortex, the insula and the lateral occipital cortex. Interestingly, the basal ganglia and the anterior cingulate cortex were involved in the subjective sense of controlling the BMI. These results inform basic neuroscience by showing that the mechanisms of BMI control extend beyond sensorimotor cortices. This knowledge may be useful for the development of BMIs that offer a more natural and embodied feeling of control for the user. Hum Brain Mapp 38:2971-2989, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Silvia Marchesotti
- Laboratory of Cognitive Neuroscience, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Laboratory of Robotic Systems, School of Engineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Roberto Martuzzi
- Laboratory of Cognitive Neuroscience, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Fondation Campus Biotech Geneva, Geneva, Switzerland
| | - Aaron Schurger
- Laboratory of Cognitive Neuroscience, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Defitech Chair in Brain-Machine Interface, School of Engineering, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Cognitive Neuroimaging Unit, NeuroSpin Research Center, INSERM, Gif-Sur-Yvette, France
| | - Maria Laura Blefari
- Laboratory of Cognitive Neuroscience, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Defitech Chair in Brain-Machine Interface, School of Engineering, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - José R Del Millán
- Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Defitech Chair in Brain-Machine Interface, School of Engineering, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Hannes Bleuler
- Laboratory of Robotic Systems, School of Engineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Olaf Blanke
- Laboratory of Cognitive Neuroscience, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Department of Neurology, University Hospital, Geneva, Switzerland
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87
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Dettmers C, Braun N, Büsching I, Hassa T, Debener S, Liepert J. [Neurofeedback-based motor imagery training for rehabilitation after stroke]. DER NERVENARZT 2017; 87:1074-1081. [PMID: 27573884 DOI: 10.1007/s00115-016-0185-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Mental training, including motor observation and motor imagery, has awakened much academic interest. The presumed functional equivalence of motor imagery and motor execution has given hope that mental training could be used for motor rehabilitation after a stroke. Results obtained from randomized controlled trials have shown mixed results. Approximately half of the studies demonstrate positive effects of motor imagery training but the rest do not show an additional benefit. Possible reasons why motor imagery training has so far not become established as a robust therapeutic approach are discussed in detail. Moreover, more recent approaches, such as neurofeedback-based motor imagery or closed-loop systems are presented and the potential importance for motor learning and rehabilitation after a stroke is discussed.
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Affiliation(s)
- C Dettmers
- Kliniken Schmieder Konstanz, Eichhornstr.68, 78464, Konstanz, Deutschland.
| | - N Braun
- Abteilung für Neuropsychologie, Department für Psychologie, Fakultät VI - Medizin und Gesundheitswissenschaften, Universität Oldenburg, Oldenburg, Deutschland
| | - I Büsching
- Kliniken Schmieder Allensbach, Allensbach, Deutschland
| | - T Hassa
- Kliniken Schmieder Allensbach, Allensbach, Deutschland.,Lurija Institut, Konstanz, Deutschland
| | - S Debener
- Abteilung für Neuropsychologie, Department für Psychologie, Fakultät VI - Medizin und Gesundheitswissenschaften, Universität Oldenburg, Oldenburg, Deutschland
| | - J Liepert
- Kliniken Schmieder Allensbach, Allensbach, Deutschland
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88
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Enriquez-Geppert S, Huster RJ, Herrmann CS. EEG-Neurofeedback as a Tool to Modulate Cognition and Behavior: A Review Tutorial. Front Hum Neurosci 2017; 11:51. [PMID: 28275344 PMCID: PMC5319996 DOI: 10.3389/fnhum.2017.00051] [Citation(s) in RCA: 124] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2016] [Accepted: 01/23/2017] [Indexed: 01/02/2023] Open
Abstract
Neurofeedback is attracting renewed interest as a method to self-regulate one’s own brain activity to directly alter the underlying neural mechanisms of cognition and behavior. It not only promises new avenues as a method for cognitive enhancement in healthy subjects, but also as a therapeutic tool. In the current article, we present a review tutorial discussing key aspects relevant to the development of electroencephalography (EEG) neurofeedback studies. In addition, the putative mechanisms underlying neurofeedback learning are considered. We highlight both aspects relevant for the practical application of neurofeedback as well as rather theoretical considerations related to the development of new generation protocols. Important characteristics regarding the set-up of a neurofeedback protocol are outlined in a step-by-step way. All these practical and theoretical considerations are illustrated based on a protocol and results of a frontal-midline theta up-regulation training for the improvement of executive functions. Not least, assessment criteria for the validation of neurofeedback studies as well as general guidelines for the evaluation of training efficacy are discussed.
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Affiliation(s)
- Stefanie Enriquez-Geppert
- Department of Clinical and Developmental Neuropsychology, Faculty of Behavioural and Social Sciences, University of Groningen Groningen, Netherlands
| | - René J Huster
- Department of Psychology, Faculty of Social Sciences, University of Oslo Oslo, Norway
| | - Christoph S Herrmann
- Experimental Psychology Laboratory, Department of Psychology, Faculty VI Medical and Health Sciences, University of Oldenburg Oldenburg, Germany
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89
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Saha S, Ahmed KI, Mostafa R, Khandoker AH, Hadjileontiadis L. Enhanced inter-subject brain computer interface with associative sensorimotor oscillations. Healthc Technol Lett 2017; 4:39-43. [PMID: 28529762 PMCID: PMC5435948 DOI: 10.1049/htl.2016.0073] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Revised: 11/13/2016] [Accepted: 11/17/2016] [Indexed: 11/19/2022] Open
Abstract
Electroencephalography (EEG) captures electrophysiological signatures of cortical events from the scalp with high-dimensional electrode montages. Usually, excessive sources produce outliers and potentially affect the actual event related sources. Besides, EEG manifests inherent inter-subject variability of the brain dynamics, at the resting state and/or under the performance of task(s), caused probably due to the instantaneous fluctuation of psychophysiological states. A wavelet coherence (WC) analysis for optimally selecting associative inter-subject channels is proposed here and is being used to boost performances of motor imagery (MI)-based inter-subject brain computer interface (BCI). The underlying hypothesis is that optimally associative inter-subject channels can reduce the effects of outliers and, thus, eliminate dissimilar cortical patterns. The proposed approach has been tested on the dataset IVa from BCI competition III, including EEG data acquired from five healthy subjects who were given visual cues to perform 280 trials of MI for the right hand and right foot. Experimental results have shown increased classification accuracy (81.79%) using the WC-based selected 16 channels compared to the one (56.79%) achieved using all the available 118 channels. The associative channels lie mostly around the sensorimotor regions of the brain, reinforced by the previous literature, describing spatial brain dynamics during sensorimotor oscillations. Apparently, the proposed approach paves the way for optimised EEG channel selection that could boost further the efficiency and real-time performance of BCI systems.
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Affiliation(s)
- Simanto Saha
- Department of Electrical and Electronic Engineering, United International University, Dhaka, Bangladesh
| | - Khawza I Ahmed
- Department of Electrical and Electronic Engineering, United International University, Dhaka, Bangladesh
| | - Raqibul Mostafa
- Department of Electrical and Electronic Engineering, United International University, Dhaka, Bangladesh
| | - Ahsan H Khandoker
- Electrical and Electronic Engineering Department, The University of Melbourne, Parkville, VIC, Australia.,Biomedical Engineering Department, Khalifa University of Science, Technology and Research, Abu Dhabi, UAE
| | - Leontios Hadjileontiadis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.,Department of Electrical and Computer Engineering, Khalifa University of Science, Technology and Research, Abu Dhabi, UAE
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90
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Use of Objective Neurocognitive Measures to Assess the Psychological States that Influence Return to Sport Following Injury. Sports Med 2016; 46:299-303. [PMID: 26604099 DOI: 10.1007/s40279-015-0435-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
There is growing interest in the effects of psychological states on human performance, especially with those who have suffered debilitating injury and are attempting to return to sport (RTS). Current research methods measure psychological states through validated questionnaires; however, these outcomes only allow for subjective assessment and may be unintentionally biased. Application of objective neurocognitive measures correlated with psychological states will advance understanding of injury outcomes by identifying human behavior and avoiding vague assumptions from subjective measures.
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91
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Mayeli A, Zotev V, Refai H, Bodurka J. Real-time EEG artifact correction during fMRI using ICA. J Neurosci Methods 2016; 274:27-37. [DOI: 10.1016/j.jneumeth.2016.09.012] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2016] [Revised: 09/08/2016] [Accepted: 09/29/2016] [Indexed: 11/17/2022]
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92
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Embodied neurofeedback with an anthropomorphic robotic hand. Sci Rep 2016; 6:37696. [PMID: 27869190 PMCID: PMC5116625 DOI: 10.1038/srep37696] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Accepted: 11/02/2016] [Indexed: 12/13/2022] Open
Abstract
Neurofeedback-guided motor imagery training (NF-MIT) has been suggested as a promising therapy for stroke-induced motor impairment. Whereas much NF-MIT research has aimed at signal processing optimization, the type of sensory feedback given to the participant has received less attention. Often the feedback signal is highly abstract and not inherently coupled to the mental act performed. In this study, we asked whether an embodied feedback signal is more efficient for neurofeedback operation than a non-embodiable feedback signal. Inspired by the rubber hand illusion, demonstrating that an artificial hand can be incorporated into one’s own body scheme, we used an anthropomorphic robotic hand to visually guide the participants’ motor imagery act and to deliver neurofeedback. Using two experimental manipulations, we investigated how a participant’s neurofeedback performance and subjective experience were influenced by the embodiability of the robotic hand, and by the neurofeedback signal’s validity. As pertains to embodiment, we found a promoting effect of robotic-hand embodiment in subjective, behavioral, electrophysiological and electrodermal measures. Regarding neurofeedback signal validity, we found some differences between real and sham neurofeedback in terms of subjective and electrodermal measures, but not in terms of behavioral and electrophysiological measures. This study motivates the further development of embodied feedback signals for NF-MIT.
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93
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Simultaneous EEG-fNIRS reveals how age and feedback affect motor imagery signatures. Neurobiol Aging 2016; 49:183-197. [PMID: 27818001 DOI: 10.1016/j.neurobiolaging.2016.10.011] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2015] [Revised: 10/07/2016] [Accepted: 10/09/2016] [Indexed: 12/18/2022]
Abstract
Stroke frequently results in motor impairment. Motor imagery (MI), the mental practice of movements, has been suggested as a promising complement to other therapeutic approaches facilitating motor rehabilitation. Of particular potential is the combination of MI with neurofeedback (NF). However, MI NF protocols have been largely optimized only in younger healthy adults, although strokes occur more frequently in older adults. The present study examined the influence of age on the neural correlates of MI supported by electroencephalogram (EEG)-based NF and on the neural correlates of motor execution. We adopted a multimodal neuroimaging framework focusing on EEG-derived event-related desynchronization (ERD%) and oxygenated (HbO) and deoxygenated hemoglobin (HbR) concentrations simultaneously acquired using functional near-infrared spectroscopy (fNIRS). ERD%, HbO concentration and HbR concentration were compared between younger (mean age: 24.4 years) and older healthy adults (mean age: 62.6 years). During MI, ERD% and HbR concentration were less lateralized in older adults than in younger adults. The lateralization-by-age interaction was not significant for movement execution. Moreover, EEG-based NF was related to an increase in task-specific activity when compared to the absence of feedback in both older and younger adults. Finally, significant modulation correlations were found between ERD% and hemodynamic measures despite the absence of significant amplitude correlations. Overall, the findings suggest a complex relationship between age and movement-related activity in electrophysiological and hemodynamic measures. Our results emphasize that the age of the actual end-user should be taken into account when designing neurorehabilitation protocols.
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94
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Duann JR, Chiou JC. A Comparison of Independent Event-Related Desynchronization Responses in Motor-Related Brain Areas to Movement Execution, Movement Imagery, and Movement Observation. PLoS One 2016; 11:e0162546. [PMID: 27636359 PMCID: PMC5026344 DOI: 10.1371/journal.pone.0162546] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2016] [Accepted: 08/24/2016] [Indexed: 12/21/2022] Open
Abstract
Electroencephalographic (EEG) event-related desynchronization (ERD) induced by movement imagery or by observing biological movements performed by someone else has recently been used extensively for brain-computer interface-based applications, such as applications used in stroke rehabilitation training and motor skill learning. However, the ERD responses induced by the movement imagery and observation might not be as reliable as the ERD responses induced by movement execution. Given that studies on the reliability of the EEG ERD responses induced by these activities are still lacking, here we conducted an EEG experiment with movement imagery, movement observation, and movement execution, performed multiple times each in a pseudorandomized order in the same experimental runs. Then, independent component analysis (ICA) was applied to the EEG data to find the common motor-related EEG source activity shared by the three motor tasks. Finally, conditional EEG ERD responses associated with the three movement conditions were computed and compared. Among the three motor conditions, the EEG ERD responses induced by motor execution revealed the alpha power suppression with highest strengths and longest durations. The ERD responses of the movement imagery and movement observation only partially resembled the ERD pattern of the movement execution condition, with slightly better detectability for the ERD responses associated with the movement imagery and faster ERD responses for movement observation. This may indicate different levels of involvement in the same motor-related brain circuits during different movement conditions. In addition, because the resulting conditional EEG ERD responses from the ICA preprocessing came with minimal contamination from the non-related and/or artifactual noisy components, this result can play a role of the reference for devising a brain-computer interface using the EEG ERD features of movement imagery or observation.
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Affiliation(s)
- Jeng-Ren Duann
- Institute of Cognitive Neuroscience, National Central University, Zhongli, Taoyuan District, Taiwan
- Institute for Neural Computation, University of California San Diego, La Jolla, California, United States of America
- * E-mail:
| | - Jin-Chern Chiou
- Department of Electrical Engineering, National Chiao Tung University, Hsinchu, Taiwan
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95
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Subramanian L, Morris MB, Brosnan M, Turner DL, Morris HR, Linden DEJ. Functional Magnetic Resonance Imaging Neurofeedback-guided Motor Imagery Training and Motor Training for Parkinson's Disease: Randomized Trial. Front Behav Neurosci 2016; 10:111. [PMID: 27375451 PMCID: PMC4896907 DOI: 10.3389/fnbeh.2016.00111] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2015] [Accepted: 05/23/2016] [Indexed: 12/18/2022] Open
Abstract
OBJECTIVE Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback (NF) uses feedback of the patient's own brain activity to self-regulate brain networks which in turn could lead to a change in behavior and clinical symptoms. The objective was to determine the effect of NF and motor training (MOT) alone on motor and non-motor functions in Parkinson's Disease (PD) in a 10-week small Phase I randomized controlled trial. METHODS Thirty patients with Parkinson's disease (PD; Hoehn and Yahr I-III) and no significant comorbidity took part in the trial with random allocation to two groups. Group 1 (NF: 15 patients) received rt-fMRI-NF with MOT. Group 2 (MOT: 15 patients) received MOT alone. The primary outcome measure was the Movement Disorder Society-Unified PD Rating Scale-Motor scale (MDS-UPDRS-MS), administered pre- and post-intervention "off-medication". The secondary outcome measures were the "on-medication" MDS-UPDRS, the PD Questionnaire-39, and quantitative motor assessments after 4 and 10 weeks. RESULTS Patients in the NF group were able to upregulate activity in the supplementary motor area (SMA) by using motor imagery. They improved by an average of 4.5 points on the MDS-UPDRS-MS in the "off-medication" state (95% confidence interval: -2.5 to -6.6), whereas the MOT group improved only by 1.9 points (95% confidence interval +3.2 to -6.8). The improvement in the intervention group meets the minimal clinically important difference which is also on par with other non-invasive therapies such as repetitive Transcranial Magnetic Stimulation (rTMS). However, the improvement did not differ significantly between the groups. No adverse events were reported in either group. INTERPRETATION This Phase I study suggests that NF combined with MOT is safe and improves motor symptoms immediately after treatment, but larger trials are needed to explore its superiority over active control conditions.
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Affiliation(s)
- Leena Subramanian
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff UniversityCardiff, UK
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityCardiff, UK
| | - Monica Busse Morris
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff UniversityCardiff, UK
| | - Meadhbh Brosnan
- Trinity College Institute of Neuroscience, Trinity CollegeDublin, Ireland
- Faculty of Psychology and Neuroscience, Maastricht UniversityMaastricht, Netherlands
| | - Duncan L. Turner
- Neurorehabilitation Unit, School of Health, Sport and Bioscience, University of East LondonLondon, UK
| | - Huw R. Morris
- Department of Clinical Neuroscience, Institute of Neurology, University College LondonLondon, UK
| | - David E. J. Linden
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff UniversityCardiff, UK
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityCardiff, UK
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96
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Structural and functional correlates of motor imagery BCI performance: Insights from the patterns of fronto-parietal attention network. Neuroimage 2016; 134:475-485. [PMID: 27103137 DOI: 10.1016/j.neuroimage.2016.04.030] [Citation(s) in RCA: 61] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Revised: 03/04/2016] [Accepted: 04/13/2016] [Indexed: 11/21/2022] Open
Abstract
Motor imagery (MI)-based brain-computer interfaces (BCIs) have been widely used for rehabilitation of motor abilities and prosthesis control for patients with motor impairments. However, MI-BCI performance exhibits a wide variability across subjects, and the underlying neural mechanism remains unclear. Several studies have demonstrated that both the fronto-parietal attention network (FPAN) and MI are involved in high-level cognitive processes that are crucial for the control of BCIs. Therefore, we hypothesized that the FPAN may play an important role in MI-BCI performance. In our study, we recorded multi-modal datasets consisting of MI electroencephalography (EEG) signals, T1-weighted structural and resting-state functional MRI data for each subject. MI-BCI performance was evaluated using the common spatial pattern to extract the MI features from EEG signals. One cortical structural feature (cortical thickness (CT)) and two measurements (degree centrality (DC) and eigenvector centrality (EC)) of node centrality were derived from the structural and functional MRI data, respectively. Based on the information extracted from the EEG and MRI, a correlation analysis was used to elucidate the relationships between the FPAN and MI-BCI performance. Our results show that the DC of the right ventral intraparietal sulcus, the EC and CT of the left inferior parietal lobe, and the CT of the right dorsolateral prefrontal cortex were significantly associated with MI-BCI performance. Moreover, the receiver operating characteristic analysis and machine learning classification revealed that the EC and CT of the left IPL could effectively predict the low-aptitude BCI users from the high-aptitude BCI users with 83.3% accuracy. Those findings consistently reveal that the individuals who have efficient FPAN would perform better on MI-BCI. Our findings may deepen the understanding of individual variability in MI-BCI performance, and also may provide a new biomarker to predict individual MI-BCI performance.
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97
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Quantifying the role of motor imagery in brain-machine interfaces. Sci Rep 2016; 6:24076. [PMID: 27052520 PMCID: PMC4823701 DOI: 10.1038/srep24076] [Citation(s) in RCA: 70] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2015] [Accepted: 03/15/2016] [Indexed: 11/08/2022] Open
Abstract
Despite technical advances in brain machine interfaces (BMI), for as-yet unknown reasons the ability to control a BMI remains limited to a subset of users. We investigate whether individual differences in BMI control based on motor imagery (MI) are related to differences in MI ability. We assessed whether differences in kinesthetic and visual MI, in the behavioral accuracy of MI, and in electroencephalographic variables, were able to differentiate between high- versus low-aptitude BMI users. High-aptitude BMI users showed higher MI accuracy as captured by subjective and behavioral measurements, pointing to a prominent role of kinesthetic rather than visual imagery. Additionally, for the first time, we applied mental chronometry, a measure quantifying the degree to which imagined and executed movements share a similar temporal profile. We also identified enhanced lateralized μ-band oscillations over sensorimotor cortices during MI in high- versus low-aptitude BMI users. These findings reveal that subjective, behavioral, and EEG measurements of MI are intimately linked to BMI control. We propose that poor BMI control cannot be ascribed only to intrinsic limitations of EEG recordings and that specific questionnaires and mental chronometry can be used as predictors of BMI performance (without the need to record EEG activity).
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98
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Ballistocardiogram artifact correction taking into account physiological signal preservation in simultaneous EEG-fMRI. Neuroimage 2016; 135:45-63. [PMID: 27012501 DOI: 10.1016/j.neuroimage.2016.03.034] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2015] [Accepted: 03/14/2016] [Indexed: 11/21/2022] Open
Abstract
The ballistocardiogram (BCG) artifact is currently one of the most challenging in the EEG acquired concurrently with fMRI, with correction invariably yielding residual artifacts and/or deterioration of the physiological signals of interest. In this paper, we propose a family of methods whereby the EEG is decomposed using Independent Component Analysis (ICA) and a novel approach for the selection of BCG-related independent components (ICs) is used (PROJection onto Independent Components, PROJIC). Three ICA-based strategies for BCG artifact correction are then explored: 1) BCG-related ICs are removed from the back-reconstruction of the EEG (PROJIC); and 2-3) BCG-related ICs are corrected for the artifact occurrences using an Optimal Basis Set (OBS) or Average Artifact Subtraction (AAS) framework, before back-projecting all ICs onto EEG space (PROJIC-OBS and PROJIC-AAS, respectively). A novel evaluation pipeline is also proposed to assess the methods performance, which takes into account not only artifact but also physiological signal removal, allowing for a flexible weighting of the importance given to physiological signal preservation. This evaluation is used for the group-level parameter optimization of each algorithm on simultaneous EEG-fMRI data acquired using two different setups at 3T and 7T. Comparison with state-of-the-art BCG correction methods showed that PROJIC-OBS and PROJIC-AAS outperformed the others when priority was given to artifact removal or physiological signal preservation, respectively, while both PROJIC-AAS and AAS were in general the best choices for intermediate trade-offs. The impact of the BCG correction on the quality of event-related potentials (ERPs) of interest was assessed in terms of the relative reduction of the standard error (SE) across trials: 26/66%, 32/62% and 18/61% were achieved by, respectively, PROJIC, PROJIC-OBS and PROJIC-AAS, for data collected at 3T/7T. Although more significant improvements were achieved at 7T, the results were qualitatively comparable for both setups, which indicate the wide applicability of the proposed methodologies and recommendations.
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99
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Zotev V, Yuan H, Misaki M, Phillips R, Young KD, Feldner MT, Bodurka J. Correlation between amygdala BOLD activity and frontal EEG asymmetry during real-time fMRI neurofeedback training in patients with depression. NEUROIMAGE-CLINICAL 2016; 11:224-238. [PMID: 26958462 PMCID: PMC4773387 DOI: 10.1016/j.nicl.2016.02.003] [Citation(s) in RCA: 106] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2015] [Revised: 01/29/2016] [Accepted: 02/10/2016] [Indexed: 10/25/2022]
Abstract
Real-time fMRI neurofeedback (rtfMRI-nf) is an emerging approach for studies and novel treatments of major depressive disorder (MDD). EEG performed simultaneously with an rtfMRI-nf procedure allows an independent evaluation of rtfMRI-nf brain modulation effects. Frontal EEG asymmetry in the alpha band is a widely used measure of emotion and motivation that shows profound changes in depression. However, it has never been directly related to simultaneously acquired fMRI data. We report the first study investigating electrophysiological correlates of the rtfMRI-nf procedure, by combining the rtfMRI-nf with simultaneous and passive EEG recordings. In this pilot study, MDD patients in the experimental group (n = 13) learned to upregulate BOLD activity of the left amygdala using an rtfMRI-nf during a happy emotion induction task. MDD patients in the control group (n = 11) were provided with a sham rtfMRI-nf. Correlations between frontal EEG asymmetry in the upper alpha band and BOLD activity across the brain were examined. Average individual changes in frontal EEG asymmetry during the rtfMRI-nf task for the experimental group showed a significant positive correlation with the MDD patients' depression severity ratings, consistent with an inverse correlation between the depression severity and frontal EEG asymmetry at rest. The average asymmetry changes also significantly correlated with the amygdala BOLD laterality. Temporal correlations between frontal EEG asymmetry and BOLD activity were significantly enhanced, during the rtfMRI-nf task, for the amygdala and many regions associated with emotion regulation. Our findings demonstrate an important link between amygdala BOLD activity and frontal EEG asymmetry during emotion regulation. Our EEG asymmetry results indicate that the rtfMRI-nf training targeting the amygdala is beneficial to MDD patients. They further suggest that EEG-nf based on frontal EEG asymmetry in the alpha band would be compatible with the amygdala-based rtfMRI-nf. Combination of the two could enhance emotion regulation training and benefit MDD patients.
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Affiliation(s)
- Vadim Zotev
- Laureate Institute for Brain Research, Tulsa, OK, USA.
| | - Han Yuan
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Masaya Misaki
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | | | | | - Matthew T Feldner
- Department of Psychological Science, University of Arkansas, Fayetteville, AR, USA
| | - Jerzy Bodurka
- Laureate Institute for Brain Research, Tulsa, OK, USA; Center for Biomedical Engineering, University of Oklahoma, Norman, OK, USA; College of Engineering, University of Oklahoma, Norman, OK, USA.
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100
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Kober SE, Gressenberger B, Kurzmann J, Neuper C, Wood G. Voluntary Modulation of Hemodynamic Responses in Swallowing Related Motor Areas: A Near-Infrared Spectroscopy-Based Neurofeedback Study. PLoS One 2015; 10:e0143314. [PMID: 26575032 PMCID: PMC4648579 DOI: 10.1371/journal.pone.0143314] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Accepted: 11/03/2015] [Indexed: 11/28/2022] Open
Abstract
In the present study, we show for the first time that motor imagery of swallowing, which is defined as the mental imagination of a specific motor act without overt movements by muscular activity, can be successfully used as mental strategy in a neurofeedback training paradigm. Furthermore, we demonstrate its effects on cortical correlates of swallowing function. Therefore, N = 20 healthy young adults were trained to voluntarily increase their hemodynamic response in swallowing related brain areas as assessed with near-infrared spectroscopy (NIRS). During seven training sessions, participants received either feedback of concentration changes in oxygenated hemoglobin (oxy-Hb group, N = 10) or deoxygenated hemoglobin (deoxy-Hb group, N = 10) over the inferior frontal gyrus (IFG) during motor imagery of swallowing. Before and after the training, we assessed cortical activation patterns during motor execution and imagery of swallowing. The deoxy-Hb group was able to voluntarily increase deoxy-Hb over the IFG during imagery of swallowing. Furthermore, swallowing related cortical activation patterns were more pronounced during motor execution and imagery after the training compared to the pre-test, indicating cortical reorganization due to neurofeedback training. The oxy-Hb group could neither control oxy-Hb during neurofeedback training nor showed any cortical changes. Hence, successful modulation of deoxy-Hb over swallowing related brain areas led to cortical reorganization and might be useful for future treatments of swallowing dysfunction.
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Affiliation(s)
- Silvia Erika Kober
- Department of Psychology, University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
- * E-mail:
| | | | | | - Christa Neuper
- Department of Psychology, University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
- Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, Graz University of Technology, Graz, Austria
| | - Guilherme Wood
- Department of Psychology, University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
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