BCI competition 2003-data set IIb: enhancing P300 wave detection using ICA-based subspace projections for BCI applications

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....

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2004-06, Vol.51 (6), p.1067-1072
Main Authors: Xu, Neng, Gao, Xiaorong, Hong, Bo, Miao, Xiaobo, Gao, Shangkai, Yang, Fusheng
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Biomedical engineering
Brain - physiology
Cognition - physiology
Computer Peripherals
Databases, Factual
Discrete wavelet transforms
Electrocardiography
Electroencephalography
Electroencephalography - classification
Electroencephalography - methods
Enterprise resource planning
Event-Related Potentials, P300 - physiology
Humans
Independent component analysis
Magnetic analysis
Pattern Recognition, Automated
Principal Component Analysis
Reproducibility of Results
Scalp
Sensitivity and Specificity
Signal processing algorithms
Signal Processing, Computer-Assisted
User-Computer Interface
Word Processing
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Summary: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.
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2004.826699