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Motor Imagery Classification Using EEG Signals for Brain-Computer Interface Applications
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Author(s): Subrota Mazumdar (Kalinga Institute of Industrial Technology University, India), Rohit Chaudhary (Kalinga Institute of Industrial Technology University, India), Suruchi Suruchi (Kalinga Institute of Industrial Technology University, India), Suman Mohanty (Kalinga Institute of Industrial Technology University, India), Divya Kumari (Kalinga Institute of Industrial Technology University, India)and Aleena Swetapadma (Kalinga Institute of Industrial Technology University, India)
Copyright: 2019
Pages: 11
Source title:
Early Detection of Neurological Disorders Using Machine Learning Systems
Source Author(s)/Editor(s): Sudip Paul (North-Eastern Hill University Shillong, India), Pallab Bhattacharya (National Institute of Pharmaceutical Education and Research (NIPER) Ahmedabad, India)and Arindam Bit (National Institute of Technology Raipur, India)
DOI: 10.4018/978-1-5225-8567-1.ch013
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Abstract
In this chapter, a nearest neighbor (k-NN)-based method for efficient classification of motor imagery using EEG for brain-computer interfacing (BCI) applications has been proposed. Electroencephalogram (EEG) signals are obtained from multiple channels from brain. These EEG signals are taken as input features and given to the k-NN-based classifier to classify motor imagery. More specifically, the chapter gives an outline of the Berlin brain-computer interface that can be operated with minimal subject change. All the design and simulation works are carried out with MATLAB software. k-NN-based classifier is trained with data from continuous signals of EEG channels. After the network is trained, it is tested with various test cases. Performance of the network is checked in terms of percentage accuracy, which is found to be 99.25%. The result suggested that the proposed method is accurate for BCI applications.
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