A Statistical Model of Brain Signals with Application to Brain-Computer Interface

This paper presents a novel approach to improving the robustness of brain-computer interfaces by using a statistical model of brain signals especially P300. We study the distributions of support vector machine scores for the signals and derive a posteriori probability model of P300/non-P300. We further derive a statistical model for multi-trial brain signals, and apply it to the rejection of undesired signals. Six subjects have been involved in an experimental study. The results demonstrate that the P300 model and the rejection method are appropriate and can help improve the robustness of the system significantly