Abstract
This paper presents a comparative study of EEG-based multiclass motor imagery classifiers based on Kullback-Leiber regularised Riemann Mean and support vector machine, hybrid one versus one classifier, linear discriminant analysis, and convolutional neural network. The paper is felt to be of inter- est to those researchers working in the motor imagery classification of EEG signals. The work presented in this paper helps to understand the basics of different multi-class motor imagery classifiers, their accuracy, and the number of channels involved.
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