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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 908591, 10 pages
Research Article

Corticomuscular Coherence Analysis on Hand Movement Distinction for Active Rehabilitation

1Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, Zhejiang 310027, China
2College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang 310027, China
3School of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang 310027, China
4Interdisciplinary Division of Biomedical Engineering, Hong Kong Polytechnic University, Hong Kong
5Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang 310027, China

Received 18 January 2013; Revised 11 March 2013; Accepted 12 March 2013

Academic Editor: Chang-Hwan Im

Copyright © 2013 Xinxin Lou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Active rehabilitation involves patient’s voluntary thoughts as the control signals of restore device to assist stroke rehabilitation. Although restoration of hand opening stands importantly in patient’s daily life, it is difficult to distinguish the voluntary finger extension from thumb adduction and finger flexion using stroke patients’ electroencephalography (EMG) on single muscle activity. We propose to implement corticomuscular coherence analysis on electroencephalography (EEG) and EMG signals on Extensor Digitorum to extract their intention involved in hand opening. EEG and EMG signals of 8 subjects are simultaneously collected when executing 4 hand movement tasks (finger extension, thumb adduction, finger flexion, and rest). We explore the spatial and temporal distribution of the coherence and observe statistically significant corticomuscular coherence appearing at left motor cortical area and different patterns within beta frequency range for 4 movement tasks. Linear discriminate analysis is applied on the coherence pattern to distinguish finger extension from thumb adduction, finger flexion, and rest. The classification results are greater than those by EEG only. The results indicate the possibility to detect voluntary hand opening based on coherence analysis between single muscle EMG signal and single EEG channel located in motor cortical area, which potentially helps active hand rehabilitation for stroke patients.