BRODU, Nicolas; LOTTE, Fabien; LÉCUYER, Anatole

doi:10.1109/CCMB.2011.5952105

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BRODU, Nicolas
Laboratoire Traitement du Signal et de l'Image [LTSI]

LOTTE, Fabien
Visualization and manipulation of complex data on wireless mobile devices [IPARLA]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Brain-Computer Interface Laboratory - Singapore [BCI]

LÉCUYER, Anatole
Virtual Reality for Improved Innovative Immersive Interaction [VR4I]

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Communication dans un congrès

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IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (SSCI'2011 CCMB), IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (SSCI'2011 CCMB), IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (SSCI'2011 CCMB), 2011-04-11, Paris. 2011-04-11p. 1-6

IEEE

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We review different techniques for extracting the power information contained in frequency bands in the context of electroencephalography (EEG) based Brain-Computer Interfaces (BCI). In this domain it is common to apply only one algorithm for extracting the power information. However previous work and our current study confirm that one may indeed expect varying degrees of success by choosing inadequate algorithms for the power extraction. Our results suggest that on average one algorithm seems superior for extracting the power information for Motor Imagery tasks : the application of a Morlet wavelet on the raw EEG signals, with the time-frequency resolution tradeoff selected by cross-validation.< Réduire

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Comparative Study of Band-Power Extraction Techniques for Motor Imagery Classification

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