An EEG-EMG correlation based brain-computer interface for hand orthosis supported neuro-rehabilitation


The use of biosignal based neurofeedback for reinforcing the motor recovery process during post-stroke rehabilitation is becoming increasingly popular in recent years. A popular technology in providing relevant neurofeedback is the brain-computer interfaces (BCI) which is generally powered by electroencephalographic (EEG) signals in the brain as this is non-invasive and safe to use. However, the connection between the brain and the muscle signals is not yet fully explored for rehabilitation purpose, despite being proved as a biomarker for motor recovery. Studies have shown that corticomuscular coherence (CMC) is a reliable measure of interaction between brain and muscle activity. However, CMC is very difficult estimate in real-time with sufficient accuracy as it requires a long duration time series to get a stable response. This makes the scope of a CMC based BCI limited for reinforcing the brain-muscle interaction during neurofeedback which may hinder motor recovery.

In order to remove such limitations, we developed a new method called correlation between band-limited power time courses (CBPT), which is capable of providing real-time neurofeedback based on the corticomuscular interaction in real-time with high accuracy. The experiment was done both on healthy individuals and hemiparetic stroke patients while the online neurofeedback was provided by CBPT and the CMC was analysed offline for comparing the accuracy between the two. Results showed that the classification accuracy was significantly high for both the healthy and patient groups in favor of CBPT.

CBPT was a better option compared to the use of cortico-muscular coherence. Also, with CBPT, in the course of a task execution to evaluate the interaction between EEG and EMG, the change in activity is more important than the magnitude of EMG activity. The difference between the features of CBPT in healthy participants and patients during a particular task is an indication that there is still a similarity between cortico-muscular coherence and CBPT. Despite this, CBPT still has an edge over cortico-muscular coherence especially when accuracies are concerned and would be a more viable option for robotic neurorehabilitation processes. The work is published in the peer-reviewed journal, Journal of Neuroscience Methods. Further study will investigate the suitability of CBPT as a biomarker for monitoring the motor recovery.


About the author

Anirban Chowdhury received the B.Tech. degree in electronics and communication engineering from Kalyani Goverment Engineering College, Kalyani, India, in 2010, and the M.Tech. degree in mechatronics and robotics from Bengal Engineering and Science University, Shibpur, India, in 2013, and Ph.D. from Indian Institute of Technology Kanpur in 2018. He worked as a postdoctoral research associate at the Intelligent Systems Research Centre at the Ulster University, UK. He is currently working as a Lecturer in Robotics and Artificial Intelligence at the School of Computer Science and Electronic Engineering at University of Essex in England. His research interests include brain–computer interfaces, biomedical signal processing, robotics, artificial intelligence, and neurorehabilitation.

About the author

Haider Raza received the B.Tech. degree in computer science and engineering from Integral University, Lucknow, India, in 2008, the M.Tech. degree in computer engineering from Manav Rachna International University, Faridabad, India, in 2011, and the Ph.D. degree in computer science from Ulster University, Derry, U.K., in 2016. He is currently a Transitional Research Fellow with the Institute for Analytics and Data Science, School of Computer Science and Electronics Engineering, University of Essex, Colchester, U.K. His current research interests include machine learning, big data, brain–computer interface, health informatics, and nonstationary learning.

About the author

Yogesh Kumar Meena received the B.Tech. and M.Tech. degrees in information technology from the Indian Institute of Information Technology and Management (IIITM), Gwalior, India, in 2010, and the Ph.D. degree in computer science from Ulster University, Londonderry, U.K., in 2018. He is currently a postdoctoral researcher at Swansea University. Previously, he was a Research Assistant with the School of Computing, Engineering & Intelligent Systems, Ulster University, United Kingdom (May 2017–May 2018) and as an Assistant Professor with the Computer Science & Information Technology Department, Sharda Group of Institution, India (July 2010–February 2014). His research interests include human–computer interaction, eye-tracking, brain–computer interface, assistive technology, robotics-based neuro-rehabilitation, machine learning, and bio-physiological signal processing.

About the author

Ashish Dutta received the Ph.D. degree in systems engineering from Akita University, Akita, Japan. From 1994 to 2000, he was with the Bhabha Atomic Research Center, Mumbai, India, where he researched on telemanipulator design and control for nuclear applications. Since 2002, he has been with the Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur, India. He was also a Visiting Professor with Nagoya University, Nagoya, Japan, in 2006 and is currently a Visiting Professor with the Kyushu Institute of Technology, Kitakyushu, Japan. His current research interests include bio-robotics, robot–human interaction, intelligent control systems, and rehabilitation engineering.

About the author

Girijesh Prasad received the B.Tech. degree in electrical engineering, in 1987, the M.Tech. degree in computer science and technology, in 1992, and the Ph.D. degree in electrical engineering from Queen’s University, Belfast, U.K., in 1997. He currently holds the post of Professor of intelligent systems with the School of Computing, Engineering, and Intelligent Systems, Ulster University, Londonderry, U.K., Magee campus. As an Executive Member of the Intelligent Systems Research Centre, Magee campus, he leads the Computational Neuroscience and Neurotechnology team. He is the Director of Northern Ireland Functional Brain Mapping facility for MEG studies. He has authored or coauthored more than 260 research papers in international journals, books, and conference proceedings. His research interests include computational intelligence, brain modelling, brain–computer interfaces and neuro-rehabilitation, and assistive robotic systems. He is a Chartered Engineer and a Fellow of the IET and International Academy of Physical Sciences. He is a Founding Member of the IEEE SMC TCs on Brain–Machine Interface Systems.


Chowdhury, A., Raza, H., Meena, Y.K., Dutta, A., and Prasad, G. An EEG-EMG correlation based brain-computer interface for hand orthosis supported neuro-rehabilitation, Journal of Neuroscience Methods 312 (2019) 1–11

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