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

Volume

3

fYear

2005

Firstpage

1535

Lastpage

1539

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);

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks and Brain, 2005. ICNN&B '05. International Conference on

Conference_Location

Beijing

Print_ISBN

0-7803-9422-4

Type

conf

DOI

10.1109/ICNNB.2005.1614924

Filename

1614924