Brain-computer interface system using approximate entropy and EMD techniques

Authors:
Qiwei Shi;Wei Zhou;Jianting Cao;Toshihisa Tanaka;Rubin Wang
Affiliations:
Saitama Institute of Technology, Saitama, Japan;Saitama Institute of Technology, Saitama, Japan;,Saitama Institute of Technology, Saitama, Japan;,Brain Science Institute, RIKEN, Saitama, Japan;East China University of Science and Technology, Shanghai, China
Venue:
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Year:
2010

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Noninvasive BCIs: Multiway Signal-Processing Array Decompositions

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Abstract

Brain-computer interface (BCI) is a technology which would enable us to communicate with external world via brain activities. The electroencephalography (EEG) now is one of the non-invasive approaches and has been widely studied for the brain computer interface. In this paper, we present a motor imaginary based BCI system. The subject's EEG data recorded during left and right wrist motor imagery is used as the input signal of BCI system. It is known that motor imagery attenuates EEG μ and β rhythms over contralateral sensorimotor cortices. Through offline analysis of the collected data, a approximate entropy (ApEn) based complexity measure is first applied to analyze the complexity between two channels located in different hemispheres. Then, empirical mode decomposition (EMD) is used to extract informative brain activity features to discriminate left and right wrist motor imagery tasks. The satisfactory results we obtained suggest that the proposed method has the potential for the classification of mental tasks in brain-computer interface system.