Conference Proceeding

Motor Imagery EEG Classification Based on Dynamic ICA Mixing Matrix

Key Lab. of Intell. Comput. & Signal Process. of MOE, Anhui Univ., Hefei, China
07/2010; DOI:10.1109/ICBBE.2010.5515719 pp.1 - 4 In proceeding of: Bioinformatics and Biomedical Engineering (iCBBE), 2010 4th International Conference on
Source: IEEE Xplore

ABSTRACT Recently the mu rhythm by motor imagination has been used as a reliable EEG pattern for brain-computer interface (BCI) system. To motor-imagery-based BCI, feature extraction and classification are two critical stages. This paper explores a dynamic ICA base on sliding window Infomax algorithm to analyze motor imagery EEG. The method can get a dynamic mixing matrix with the new data inputting, which is unlike the static mixing matrix in traditional ICA algorithm. And by using the feature patterns based on total energy of dynamic mixing matrix coefficients in a certain time window, the classification accuracy without training can be achieved beyond 85% for BCI competition 2003 data set III. The results demonstrate that the method can be used for the extraction and classification of motor imagery EEG. In the present study, it suggests that the proposed algorithm may provide a valuable alternative to study motor imagery EEG for BCI applications.

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Keywords

brain-computer interface
 
certain time window
 
classification accuracy
 
feature extraction
 
feature patterns
 
matrix coefficients
 
motor imagery EEG
 
motor imagination
 
motor-imagery-based BCI
 
mu rhythm
 
new data inputting
 
paper explores
 
proposed algorithm
 
reliable EEG pattern
 
study motor imagery EEG
 
total energy
 
traditional ICA algorithm
 
valuable alternative
 
window Infomax algorithm