EEG Subspace Analysis and Classification Using Principal Angles for Brain-Computer Interfaces

Brain-Computer Interfaces (BCIs) help paralyzed people who have lost some or all of their ability to communicate and control the outside environment from loss of voluntary muscle control. Most BCIs are based on the classification of multichannel electroencephalography (EEG) signals recorded from users as they respond to external stimuli or perform various mental activities. The classification process is fraught with difficulties caused by electrical noise, signal artifacts, and nonstationarity. One approach to reducing the effects of similar difficulties in other domains is the use of principal angles between subspaces, which has been applied mostly to video sequences. In this paper, it is shown that principal angles are also a useful approach to the classification of EEG signals that are recorded during a BCI typing application. Single letters are flashed on a computer display every second as the subject counts the number of times the desired letter appears. The appearance of the subject's desired letter is detected by identifying a P300-wave within a one-second window of EEG following the flash of a letter. Classification of pairs of one-second windows of EEG resulted in an average accuracy of detecting the P300 of 88% for a motor-impaired subject recorded in their home and 76% for an unimpaired subject recorded in the lab.