Proposal of Brain-Computer Interface Architecture to Command a Robotic Wheelchair

This paper presents a Brain-Computer Interface architecture that is being implemented in a robotic wheelchair. The interface uses electroencephalographic signals and works with three mental tasks, which are the imagination of right or left hand movements and generation of words beginning with the same random letter. This research uses a data set to perform a simulation of real-time classification, which is the pseudo-online technique, in order to have a preliminary view of the performance of the proposed BCI architecture. Linear Discriminant Analysis is used to recognize the mental tasks. The feature extraction uses the Power Spectral Density and the choice of EEG channel and frequency uses the Kullback-Leibler symmetric divergence. A reclassification model is proposed to stabilize the classifier, and the Sammon map is used to visualize the class separation.