Design, implementation and evaluation of a real-time p300-based brain-computer interface system

Official URL: http://192.168.1.20/record=b1301369 (Table of Contents)

In this thesis, we present a new end-to-end brain-computer interface system based on electroencephalography (EEG). Our system exploits the P300 signal in the brain, a positive deflection in event-related potentials, caused by rare events. P300 can be used for various tasks, perhaps the most well-known being a spelling device. We have designed a flexible visual stimulus mechanism that can be adapted to user preferences. We have developed and implemented EEG signal processing, learning and classification algorithms. Our classifier is based on Bayes linear discriminant analysis, in which we have explored various choices and improvements. We have designed data collection experiments for offline and online decision-making. We have proposed modifications in the stimulus and decision-making procedure to increase online efficiency. We have evaluated the performance of our system on 8 healthy subjects on a spelling task and have observed that our system achieves higher average speed than state-of-the-art systems reported in the literature for a given classification accuracy.