Multiclass mi-task classification using logistic regression and filter bank common spatial patterns

Abstract

We proposed a classification technique of EEG motor imagery signals using Logistic regression and feature extraction algorithm using filter bank common spatial pattern (FBCSP). Main theme of FBCSP is that the signals decomposed into 5 sub band then calculated CSP for each sub band, this algorithm also allows automated frequency band selection. We combined each subband CSP feature vector, feed this feature vector into machine learning algorithm. In the paper Logistic regression is used to classify among multiple classes. To evaluate this method, we used here publicly available dataset namely Brain-Computer Interface competition IV-2a. Because of high accuracy and kappa that shown in accuracy table that proposed method is promising.