EEG-based classification of brain activity for brightness stimuli

Abstract

Brain-computer interface (BCI) is providing a new channel for human to interact with computers and devices. Researches have been conducted on motor imaginary detection to aid individuals who cannot use any motor system for communication, to operate the computer and other devices by prosthesis. In this paper, we developed a classifier that can distinguish EEG signals responding to visual stimuli. Brightness difference was used as a preliminary case study of this approach as it is easily controllable and provides stable stimuli. The brain activities were measured by an electroencephalogram (EEG) system, and an adaptive auto-regressive (AAR) model was utilized to extract the features of different brain states corresponding to stimuli in different brightness. A minimum distance analysis (MDA) classifier was created to discriminate different brightness perception. By this means, different perceptions of four brightness conditions were decoded successfully based on the brain activities of single trials. © 2009 Springer Berlin Heidelberg.