Improvements of a brain-computer interface applied to a robotic wheelchair

Abstract

Two distinct signal features suitable to be used as input to a Support-Vector Machine (SVM) classifier in an application involving hands motor imagery and the correspondent EEG signal are evaluated in this paper. Such features are the Power Spectral Density (PSD) components and the Adaptive Autoregressive (AAR) parameters. The best result (an accuracy of 97.1%) is obtained when using PSD components, while the AAR parameters generated an accuracy of 91.4%. The results also demonstrate that it is possible to use only two EEG channels (bipolar configuration around C 3 and C 4), discarding the bipolar configuration around C z . The algorithms were tested with a proprietary EEG data set involving 4 individuals and with a data set provided by the University of Graz (Austria) as well. The resulting classification system is now being implemented in a Brain-Computer Interface (BCI) used to guide a robotic wheelchair. © 2010 Springer-Verlag Berlin Heidelberg.