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Transition detection for brain computer interface classification

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URI: http://hdl.handle.net/10016/6751
ISSN: 1865-0929
ISBN: 978-3-642-11720-6
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transition_aler_2009_ps.pdf (523.34 KB)
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2010
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Springer
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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.
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Proceeding of: Biosignals 2009. International Conference on Bio-inspired Systems and Signal Processing, BIOSTEC 2009. Porto (Portugal), 14-17 January 2009
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Brain computer interface
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Biomedical Engineering Systems and Technologies International Joint Conference, BIOSTEC 2009 Porto, Portugal, January 14-17, 2009, Revised Selected Papers, p. 200-210
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