Title :
T-weighted Approach for Neural Information Processing in P300 based Brain-Computer Interface
Author :
Liu, Yang ; Zhou, Zongtan ; Hu, Dewen ; Dong, Guohua
Author_Institution :
Dept. of Autom. Control, Nat. Univ. of Defense Technol., Changsha
Abstract :
A novel method for feature extraction based on T-statistic criterion is put forward and introduced for P300 potential detection in brain-computer interface (BCI) applications. After decorrelation by principal component analysis (PCA), the optimized weighted sum of EEG signal is computed to construct the features. Applied to P300 speller paradigm of BCI competition 2003 and BCI competition III (2005), this method achieved character accuracy of 100% and 90% respectively, and by the latter score our group got the third place for the P300 dataset (dataset II) in the BCI competition III
Keywords :
electroencephalography; feature extraction; human computer interaction; medical signal processing; neural nets; neurophysiology; principal component analysis; user interfaces; EEG signal; P300; T-statistic criterion; T-weighted approach; brain-computer interface; feature extraction; neural information processing; principal component analysis; speller paradigm; Brain computer interfaces; Continuous wavelet transforms; Electroencephalography; Independent component analysis; Information processing; Principal component analysis; Scalp; Signal processing; Support vector machine classification; Support vector machines; Electroencephalography (EEG); P300 potential; T-weight; brain-computer interface (BCI);
Conference_Titel :
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-9422-4
DOI :
10.1109/ICNNB.2005.1614924
