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EEG Feature Extraction and Pattern Classification Based on Motor Imagery in Brain-Computer Interface

EEG Feature Extraction and Pattern Classification Based on Motor Imagery in Brain-Computer Interface

Ling Zou, Xinguang Wang, Guodong Shi, Zhenghua Ma
Copyright: © 2011 |Volume: 3 |Issue: 3 |Pages: 14
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781613509197|DOI: 10.4018/ijssci.2011070104
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MLA

Zou, Ling, et al. "EEG Feature Extraction and Pattern Classification Based on Motor Imagery in Brain-Computer Interface." IJSSCI vol.3, no.3 2011: pp.43-56. http://doi.org/10.4018/ijssci.2011070104

APA

Zou, L., Wang, X., Shi, G., & Ma, Z. (2011). EEG Feature Extraction and Pattern Classification Based on Motor Imagery in Brain-Computer Interface. International Journal of Software Science and Computational Intelligence (IJSSCI), 3(3), 43-56. http://doi.org/10.4018/ijssci.2011070104

Chicago

Zou, Ling, et al. "EEG Feature Extraction and Pattern Classification Based on Motor Imagery in Brain-Computer Interface," International Journal of Software Science and Computational Intelligence (IJSSCI) 3, no.3: 43-56. http://doi.org/10.4018/ijssci.2011070104

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Abstract

Accurate classification of EEG left and right hand motor imagery is an important issue in brain-computer interface. Firstly, discrete wavelet transform method was used to decompose the average power of C3 electrode and C4 electrode in left-right hands imagery movement during some periods of time. The reconstructed signal of approximation coefficient A6 on the sixth level was selected to build up a feature signal. Secondly, the performances by Fisher Linear Discriminant Analysis with two different threshold calculation ways and Support Vector Machine methods were compared. The final classification results showed that false classification rate by Support Vector Machine was lower and gained an ideal classification results.

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