Abstract:Abstract. This paper deals with the classifi ation 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 classif cation 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… Show more
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