Motor-Imagery based EEG Signals Classification using MLP and KNN Classifiers

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2021 by IJETT Journal
Volume-69 Issue-1
Year of Publication : 2021
Authors : Yogendra Narayan
DOI :  10.14445/22315381/IJETT-V69I1P219


MLA Style: Yogendra Narayan. "Motor-Imagery based EEG Signals Classification using MLP and KNN Classifiers" International Journal of Engineering Trends and Technology 69.1(2021):121-125. 

APA Style:Yogendra Narayan. Motor-Imagery based EEG Signals Classification using MLP and KNN Classifiers  International Journal of Engineering Trends and Technology, 69(1), 121-125.

The electroencephalogram (EEG) signals classification plays a major role in developing assistive rehabilitation devices for physically disabled performs. In this context, EEG data were acquired from 20 healthy humans followed by the pre-processing and feature extraction process. After extracting the 12-time domain features, two well-known classifiers, namely K-nearest neighbor (KNN) and multi-layer perceptron (MLP), were employed. The fivefold cross-validation approach was utilized for dividing data into training and testing purposes. The results indicated that the performance of the MLP classifier was found better than the KNN classifier. MLP classifier achieved 95% classifier accuracy, which is the best. The outcome of this study would be very useful for the online development of the EEG classification model and designing the EEG based wheelchair.

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Motor-Imagery, EEG signal, KNN, MLP, ICA, Innovation