ECoG classification based on wavelet variance / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 460-463, 2013.
Article
in Chinese
| WPRIM
| ID: wpr-234630
ABSTRACT
For a typical electrocorticogram (ECoG)-based brain-computer interface (BCI) system in which the subject's task is to imagine movements of either the left small finger or the tongue, we proposed a feature extraction algorithm using wavelet variance. Firstly the definition and significance of wavelet variance were brought out and taken as feature based on the discussion of wavelet transform. Six channels with most distinctive features were selected from 64 channels for analysis. Consequently the EEG data were decomposed using db4 wavelet. The wavelet coeffi-cient variances containing Mu rhythm and Beta rhythm were taken out as features based on ERD/ERS phenomenon. The features were classified linearly with an algorithm of cross validation. The results of off-line analysis showed that high classification accuracies of 90. 24% and 93. 77% for training and test data set were achieved, the wavelet vari-ance had characteristics of simplicity and effectiveness and it was suitable for feature extraction in BCI research. K
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Physiology
/
Algorithms
/
Signal Processing, Computer-Assisted
/
Cerebral Cortex
/
Electroencephalography
/
Wavelet Analysis
/
Brain-Computer Interfaces
/
Methods
Limits:
Humans
Language:
Chinese
Journal:
Journal of Biomedical Engineering
Year:
2013
Type:
Article
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