Comparison of multi-class motor imagery classification methods for EEG signals

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 classifier, linear discriminant analysis, convolutional neural network. The is felt to be inter- est those researchers working in the classification EEG signals. work presented this helps understand basics different multi-class classifiers, their accuracy, number channels involved.