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A Review on Motor Imagery Signal Classification for BCI
Rupal Chaudhari, Hiren J. Galiyawala
Pages - 16 - 34     |    Revised - 30-04-2017     |    Published - 01-06-2017
Volume - 11   Issue - 2    |    Publication Date - June 2017  Table of Contents
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KEYWORDS
Electroencephalogram (EEG), Brain Computer Interface, Motor Imagery BCI, EEG Signal Classification, Motor Imagery Signal Classification for BCI.
ABSTRACT
Brain computer interface (BCI) is an evolving technology from past few years. Scalp recorded electroencephalogram (EEG) based BCI technologies are widely used because of safety, low cost and portability. Millions of people are suffering from stroke worldwide and become disabled. They may lose communication control and fall into the locked in state (LIS) or completely locked in state (CLIS). Motor imagery brain computer interface (MI-BCI) can provide non-muscular channel for communication to those who are suffering from neuronal disorders, only by imagination of different motor tasks e.g. left-right hand and foot movement imagination. EEG signals are time varying, non-stationary random signals which are changes in person to person and occurs at different frequencies. For real time application of such a system efficient classification of motor tasks is required. The biggest challenge in MI-BCI system design is extraction of robust, informative and discriminative features which can be converted into device commands. The main application of MI-BCI is neurorehabilitation and control of wheelchair or robotic limbs. The objective of this paper is to give brief information about different stages of EEG based MI-BCI system. It also includes the review on motor imagery signal classification.
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Miss Rupal Chaudhari
Department of Electronics & Communication Engineering CGPIT, Uka Tarsadia University Surat, India - India
rupalec19@gmail.com
Mr. Hiren J. Galiyawala
Department of Electronics & Communication Engineering CGPIT, Uka Tarsadia University Surat, India - India