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. Applied to the dataset IIb of BCI Competition 2003, the algorithm achieved an accuracy of 100% in P300 detection within five repetitions.
Xu
Neng
N
Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China. xuneng01@mails.tsinghua.edu.cn
Gao
Xiaorong
X
Hong
Bo
B
Miao
Xiaobo
X
Gao
Shangkai
S
Yang
Fusheng
F
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology*; D003071:Cognition / Q000502:physiology*; D016287:Computer Peripherals; D016208:Databases, Factual; D004569:Electroencephalography / Q000145:classification / Q000379:methods*; D018913:Event-Related Potentials, P300 / Q000502:physiology*; D006801:Humans; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface; D015443:Word Processing