Generalizing to new subjects in brain-computer interfacing

This paper evaluates an algorithm based on support vector machines to analyze EEG data from the P300 speller brain-computer interface paradigm. We evaluated the performance of this technique on own experimental data from 8 subjects and achieved high transfer rates of up to 97.57 bits/min (mean 47.26 bits/min) within subjects. We then investigated how well the classifier generalizes when it is trained on data from a set of several subjects and then applied on data from a new subject to use this BCI in a pretrained fashion. Transfer rates up to 61.04 bits/min were achieved (mean 17.64 bits/min) for this situation indicating an encouraging generalization performance.