Abstract
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.
Publication types
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Comparative Study
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Evaluation Study
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Research Support, Non-U.S. Gov't
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Validation Study
MeSH terms
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Algorithms*
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Artificial Intelligence*
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Brain / physiology*
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Cognition / physiology*
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Computer Peripherals
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Databases, Factual
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Electroencephalography / classification
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Electroencephalography / methods*
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Event-Related Potentials, P300 / physiology*
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Humans
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Pattern Recognition, Automated
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Principal Component Analysis
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Reproducibility of Results
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Sensitivity and Specificity
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Signal Processing, Computer-Assisted
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User-Computer Interface*
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Word Processing