Performance of channel selection used for Multi-class EEG signal classification of motor imagery

Abstract

In this paper, a study of a non-invasive brain-machine interfaces for the classification of 4 imaginary are presented. Performance comparisons using time-frequency analysis between the Linear Discriminant Analysis motor activities (left hand, right hand, foot, tongue) with the BCI competition III dataset IIIa is (LDA), the Support Vector Machine (SVM) and the K-Nearest Neighbors (KNN) algorithms have been carried. The number and position of electrodes for each subject were investigated to provide an improvement for the classification accuracy of the algorithm. Results show that the electrode positions varied from subject to subject; moreover , using one subset of the channels enhanced the classification performances compared to literature data. an average accuracy of 86.06% was observed among all 3 subjects.