Performance evaluation of five classification algorithms in low-dimensional feature vectors extracted from EEG signals

There are lots of classification and feature extraction algorithms in the field of brain computer interface. It is significant to use optimal classification algorithm and fewer features to implement a fast and accurate brain computer interface system. In this paper, we evaluate the performances of five classical classifiers in different aspects including classification accuracy, sensitivity, specificity, Kappa and computational time in low-dimensional feature vectors extracted from EEG signals. The experiments show that naive Bayes is the most appropriate classifier for low dimensional feature vectors compared to k-nearest neighbor, support vector machine, linear discriminant analysis and decision tree classifiers.

I. Introduction

THE brain computer interface (BCI) provides a new communication channel for subjects to interact the external world without using their muscles. Electroencephalogram (EEG) based BCI systems analyzed electrical brain activity recorded from electrodes placed the subject's scalp and extract features to determine the intents of the user. Then, translate them into the control signals that are used to control external devices (e.g., an electromechanical arm, a wheelchair) [1], [2].