An EEG-EMG Correlation-based Brain-Computer Interface for Hand Orthosis Supported Neuro-Rehabilitation

Anirban Chowdhury, Haider Raza, Yogesh Kumar Meena, Ashish Dutta, Girijesh Prasad

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Background: Corticomuscular coupling has been investigated for long, to find out the underlying mechanisms behind cortical drives to produce different motor tasks. Although important in rehabilitation perspective, the use of corticomuscular coupling for driving brain-computer interface (BCI)-based neurorehabilitation is much ignored. This is primarily due to the fact that the EEG-EMG coherence popularly used to compute corticomuscular coupling, fails to produce sufficient accuracy in single-trial based prediction of motor tasks in a BCI system. New method: In this study, we have introduced a new corticomuscular feature extraction method based on the correlation between band-limited power time-courses (CBPT) associated with EEG and EMG. 16 healthy individuals and 8 hemiplegic patients participated in a BCI-based hand orthosis triggering task, to test the performance of the CBPT method. The healthy population was equally divided into two groups; one experimental group for CBPT-based BCI experiment and another control group for EEG-EMG coherence based BCI experiment. Results: The classification accuracy of the CBPT-based BCI system was found to be 92.81 ± 2.09% for the healthy experimental group and 84.53 ± 4.58% for the patients’ group. Comparison with existing method: The CBPT method significantly (p-value < 0.05) outperformed the conventional EEG-EMG coherence method in terms of classification accuracy. Conclusions: The experimental results clearly indicate that the EEG-EMG CBPT is a better alternative as a corticomuscular feature to drive a BCI system. Additionally, it is also feasible to use the proposed method to design BCI-based robotic neurorehabilitation paradigms.

LanguageEnglish
Pages1-11
Number of pages11
JournalJournal of Neuroscience Methods
Volume312
Issue number1
Early online date16 Nov 2018
DOIs
Publication statusPublished - 15 Jan 2019

Fingerprint

Brain-Computer Interfaces
Orthotic Devices
Electroencephalography
Rehabilitation
Hand
Computer Systems
Robotics
Control Groups

Keywords

  • Correlation between band-limited power time-courses (CBPT)
  • Corticomuscular-coherence (CMC)
  • Electroencephalogram (EEG)
  • Electromyogram (EMG)
  • Hand orthosis
  • Hybrid brain-computer interface (h-BCI)
  • Neurorehabilitation

Cite this

Chowdhury, Anirban ; Raza, Haider ; Meena, Yogesh Kumar ; Dutta, Ashish ; Prasad, Girijesh. / An EEG-EMG Correlation-based Brain-Computer Interface for Hand Orthosis Supported Neuro-Rehabilitation. In: Journal of Neuroscience Methods. 2019 ; Vol. 312, No. 1. pp. 1-11.
@article{9e40c8fc39994a4ba5018057117d7ed9,
title = "An EEG-EMG Correlation-based Brain-Computer Interface for Hand Orthosis Supported Neuro-Rehabilitation",
abstract = "Background: Corticomuscular coupling has been investigated for long, to find out the underlying mechanisms behind cortical drives to produce different motor tasks. Although important in rehabilitation perspective, the use of corticomuscular coupling for driving brain-computer interface (BCI)-based neurorehabilitation is much ignored. This is primarily due to the fact that the EEG-EMG coherence popularly used to compute corticomuscular coupling, fails to produce sufficient accuracy in single-trial based prediction of motor tasks in a BCI system. New method: In this study, we have introduced a new corticomuscular feature extraction method based on the correlation between band-limited power time-courses (CBPT) associated with EEG and EMG. 16 healthy individuals and 8 hemiplegic patients participated in a BCI-based hand orthosis triggering task, to test the performance of the CBPT method. The healthy population was equally divided into two groups; one experimental group for CBPT-based BCI experiment and another control group for EEG-EMG coherence based BCI experiment. Results: The classification accuracy of the CBPT-based BCI system was found to be 92.81 ± 2.09{\%} for the healthy experimental group and 84.53 ± 4.58{\%} for the patients’ group. Comparison with existing method: The CBPT method significantly (p-value < 0.05) outperformed the conventional EEG-EMG coherence method in terms of classification accuracy. Conclusions: The experimental results clearly indicate that the EEG-EMG CBPT is a better alternative as a corticomuscular feature to drive a BCI system. Additionally, it is also feasible to use the proposed method to design BCI-based robotic neurorehabilitation paradigms.",
keywords = "Correlation between band-limited power time-courses (CBPT), Corticomuscular-coherence (CMC), Electroencephalogram (EEG), Electromyogram (EMG), Hand orthosis, Hybrid brain-computer interface (h-BCI), Neurorehabilitation",
author = "Anirban Chowdhury and Haider Raza and Meena, {Yogesh Kumar} and Ashish Dutta and Girijesh Prasad",
year = "2019",
month = "1",
day = "15",
doi = "10.1016/j.jneumeth.2018.11.010",
language = "English",
volume = "312",
pages = "1--11",
journal = "Journal of Neuroscience Methods",
issn = "0165-0270",
publisher = "Elsevier",
number = "1",

}

An EEG-EMG Correlation-based Brain-Computer Interface for Hand Orthosis Supported Neuro-Rehabilitation. / Chowdhury, Anirban; Raza, Haider; Meena, Yogesh Kumar; Dutta, Ashish; Prasad, Girijesh.

In: Journal of Neuroscience Methods, Vol. 312, No. 1, 15.01.2019, p. 1-11.

Research output: Contribution to journalArticle

TY - JOUR

T1 - An EEG-EMG Correlation-based Brain-Computer Interface for Hand Orthosis Supported Neuro-Rehabilitation

AU - Chowdhury, Anirban

AU - Raza, Haider

AU - Meena, Yogesh Kumar

AU - Dutta, Ashish

AU - Prasad, Girijesh

PY - 2019/1/15

Y1 - 2019/1/15

N2 - Background: Corticomuscular coupling has been investigated for long, to find out the underlying mechanisms behind cortical drives to produce different motor tasks. Although important in rehabilitation perspective, the use of corticomuscular coupling for driving brain-computer interface (BCI)-based neurorehabilitation is much ignored. This is primarily due to the fact that the EEG-EMG coherence popularly used to compute corticomuscular coupling, fails to produce sufficient accuracy in single-trial based prediction of motor tasks in a BCI system. New method: In this study, we have introduced a new corticomuscular feature extraction method based on the correlation between band-limited power time-courses (CBPT) associated with EEG and EMG. 16 healthy individuals and 8 hemiplegic patients participated in a BCI-based hand orthosis triggering task, to test the performance of the CBPT method. The healthy population was equally divided into two groups; one experimental group for CBPT-based BCI experiment and another control group for EEG-EMG coherence based BCI experiment. Results: The classification accuracy of the CBPT-based BCI system was found to be 92.81 ± 2.09% for the healthy experimental group and 84.53 ± 4.58% for the patients’ group. Comparison with existing method: The CBPT method significantly (p-value < 0.05) outperformed the conventional EEG-EMG coherence method in terms of classification accuracy. Conclusions: The experimental results clearly indicate that the EEG-EMG CBPT is a better alternative as a corticomuscular feature to drive a BCI system. Additionally, it is also feasible to use the proposed method to design BCI-based robotic neurorehabilitation paradigms.

AB - Background: Corticomuscular coupling has been investigated for long, to find out the underlying mechanisms behind cortical drives to produce different motor tasks. Although important in rehabilitation perspective, the use of corticomuscular coupling for driving brain-computer interface (BCI)-based neurorehabilitation is much ignored. This is primarily due to the fact that the EEG-EMG coherence popularly used to compute corticomuscular coupling, fails to produce sufficient accuracy in single-trial based prediction of motor tasks in a BCI system. New method: In this study, we have introduced a new corticomuscular feature extraction method based on the correlation between band-limited power time-courses (CBPT) associated with EEG and EMG. 16 healthy individuals and 8 hemiplegic patients participated in a BCI-based hand orthosis triggering task, to test the performance of the CBPT method. The healthy population was equally divided into two groups; one experimental group for CBPT-based BCI experiment and another control group for EEG-EMG coherence based BCI experiment. Results: The classification accuracy of the CBPT-based BCI system was found to be 92.81 ± 2.09% for the healthy experimental group and 84.53 ± 4.58% for the patients’ group. Comparison with existing method: The CBPT method significantly (p-value < 0.05) outperformed the conventional EEG-EMG coherence method in terms of classification accuracy. Conclusions: The experimental results clearly indicate that the EEG-EMG CBPT is a better alternative as a corticomuscular feature to drive a BCI system. Additionally, it is also feasible to use the proposed method to design BCI-based robotic neurorehabilitation paradigms.

KW - Correlation between band-limited power time-courses (CBPT)

KW - Corticomuscular-coherence (CMC)

KW - Electroencephalogram (EEG)

KW - Electromyogram (EMG)

KW - Hand orthosis

KW - Hybrid brain-computer interface (h-BCI)

KW - Neurorehabilitation

UR - http://www.scopus.com/inward/record.url?scp=85056853103&partnerID=8YFLogxK

U2 - 10.1016/j.jneumeth.2018.11.010

DO - 10.1016/j.jneumeth.2018.11.010

M3 - Article

VL - 312

SP - 1

EP - 11

JO - Journal of Neuroscience Methods

T2 - Journal of Neuroscience Methods

JF - Journal of Neuroscience Methods

SN - 0165-0270

IS - 1

ER -