Online classification of two mental tasks using a SVM-based BCI system

Abstract

A Brain-Computer Interface (BCI) can be very useful to help people with several movement disabilities to improve their independence or to assist them in rehabilitation tasks. In this paper, the results of the online classification of two mental tasks from electroencephalographic signals (EEG) are shown. The objective of this paper is to determine whether the accuracy in the online differentiation of two mental tasks could be enough to command a robot arm using two mental tasks. The results demonstrate that the features obtained using periodogram (a Power Spectral Density estimation) and the classification of these using a SVM-based (Support Vector Machine) system can be used in a reliable control of a robot arm. For all the users, the accuracy is around 87±2%. This accuracy is enough to be used to this end in real time.