BCI competition 2003-data set IIb: enhancing P300 wave detection using ICA-based subspace projections for BCI applications
IEEE transactions on biomedical engineering, 2004•ieeexplore.ieee.org
An algorithm based on independent component analysis (ICA) is introduced for P300
detection. After ICA decomposition, P300-related independent components are selected
according to the a priori knowledge of P300 spatio-temporal pattern, and clear P300 peak is
reconstructed by back projection of ICA. Applied to the dataset IIb of BCI Competition 2003,
the algorithm achieved an accuracy of 100% in P300 detection within five repetitions.
detection. After ICA decomposition, P300-related independent components are selected
according to the a priori knowledge of P300 spatio-temporal pattern, and clear P300 peak is
reconstructed by back projection of ICA. Applied to the dataset IIb of BCI Competition 2003,
the algorithm achieved an accuracy of 100% in P300 detection within five repetitions.
An algorithm based on independent component analysis (ICA) is introduced for P300 detection. After ICA decomposition, P300-related independent components are selected according to the a priori knowledge of P300 spatio-temporal pattern, and clear P300 peak is reconstructed by back projection of ICA. Applied to the dataset IIb of BCI Competition 2003, the algorithm achieved an accuracy of 100% in P300 detection within five repetitions.
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