RT Conference Proceedings
T1 Transition detection for brain computer interface classification
A1 Aler, Ricardo
A1 Galván, Inés M.
A1 Valls, José M.
AB 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.
PB Springer
SN 978-3-642-11720-6
SN 1865-0929
YR 2010
FD 2010
LK https://hdl.handle.net/10016/6751
UL https://hdl.handle.net/10016/6751
LA eng
NO Proceeding of: Biosignals 2009. International Conference on Bio-inspired Systems and Signal Processing, BIOSTEC 2009. Porto (Portugal), 14-17 January 2009
DS e-Archivo
RD 4 may. 2024