Combining Features for BCI

Recently, interest is growing to develop an effective communication interface connectingthe human brain to a computer, the'Brain-Computer Interface' (BCI). One motivation of BCI research is to provide a new communication channel substituting normal motor output in patients with severe neuromuscular disabilities. In the last decade, various neurophysiological corticalprocesses, such as slow potential shifts, movement related potentials (MRPs) or event-related desynchronization (ERD) of spontaneous EEG rhythms, were shown to be suitable for BCI, and, consequently, differentindependent approaches of extracting BCI-relevant EEGfeatures for single-trial analysis are under investigation. Here, we present and systematically compare several concepts for combining such EEGfeatures to improve the single-trial classification. Feature combinations areevaluated on movement imagination experiments with 3 subjects where EEGfeatures are based on either MRPs or ERD, or both. Those combination methods that incorporate the assumption that the single EEG-featuresare physiologically mutually independent outperform the plain method of'adding' evidence where the single-feature vectors are simply concatenated. These results strengthen the hypothesis that MRP and ERD reflect at least partially independent aspects of cortical processes and open a new perspective to boost BCI effectiveness.