Transition detection for brain computer interface classification

Abstract

This paper deals with the classification of signals for brain-computer interfaces (BCI). We take advantage of the fact that thoughts last for a period, and therefore EEG samples run in sequences belonging to the same class (thought). Thus, the classification problem can be reformulated into two subproblems: detecting class transitions and determining the class for sequences of samples between transitions. The method detects transitions when the L1 norm between the power spectra at two different times is larger than a threshold. To tackle the second problem, samples are classified by taking into account a window of previous predictions. Two types of windows have been tested: a constant-size moving window and a variable-size growing window. In both cases, results are competitive with those obtained in the BCI III competition. © 2010 Springer-Verlag Berlin Heidelberg.