Transition detection for brain computer interface classification

Abstract

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: de- tecting class transitions and determining the class for sequences of samples be- tween 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 sec- ond problem, samples are classified by taking into account a window of previous predictions. Two types of windows have been tested: a constant-size moving win- dow and a variable-size growing window. In both cases, results are competitive with those obtained in the BCI III competition.