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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. Index Terms-Brain-computer interface (BCI), independent component analysis, infomax, P300 detection.doi:10.1109/tbme.2004.826699 pmid:15188880 fatcat:audyc4jckrg35bkgx2mqgz7xzy