Motor-Imagery based EEG Signals Classification using MLP and KNNClassifiers
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Abstract
The electro encephalo gram (EEG) signals classification playsa 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 purpose. The results indicated that the performance of 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 online development of EEG classification model as well as designing the EEG based wheelchair.
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