EEG Classification for BCI Based on CSP and SVM-GA

Hao Yang; Song Wu

doi:10.4028/www.scientific.net/AMM.459.228

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 459EEG Classification for BCI Based on CSP and SVM-GA

EEG Classification for BCI Based on CSP and SVM-GA

Abstract:

Electroencephalogram (EEG) is generally used in Brain-Computer Interface (BCI) applications to measure the brain signals. However, the multichannel EEG signals characterized by unrelated and redundant features will deteriorate the classification accuracy. This paper presents a method based on common spatial pattern (CSP) for feature extraction and support vector machine with genetic algorithm (SVM-GA) as a classifier, the GA is used to optimize the kernel parameters setting. The proposed algorithm is performed on data set Iva of BCI Competition III. Results show that the proposed method outperforms the conventional linear discriminant analysis (LDA) in average classification performance.

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Periodical:

Applied Mechanics and Materials (Volume 459)

Pages:

228-231

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.459.228

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Online since:

October 2013

Authors:

Hao Yang*, Song Wu

Keywords:

Brain Computer Interface (BCI), Common Spatial Pattern, Genetic Algorithm (GA), Support Vector Machine (SVM)

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References

[1] Krusienski, D.J., et al., Critical issues in state-of-the-art brain-computer interface signal processing. Journal of Neural Engineering, 2011. 8(0250022SI).

Google Scholar

[2] Blankertz, B., et al., Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Processing Magezine, 2008. 25(1): pp.41-56.

DOI: 10.1109/msp.2008.4408441

Google Scholar

[3] Lotte, F., et al., A review of classification algorithms for EEG-based brain-computer interfaces. Journal of Neural Engineering, 2007. 4(2): p. R1-R13.

DOI: 10.1088/1741-2560/4/2/r01

Google Scholar

[4] Rakotomamonjy, A. and V. Guigue, BCI Competition III: Dataset II- Ensemble of SVMs for BCI P300 Speller. IEEE Transactions on Biomedical Engineering, 2008. 55(3): pp.1147-1154.

DOI: 10.1109/tbme.2008.915728

Google Scholar

[5] Bamdadian, A., et al. Real coded GA-based SVM for motor imagery classification in a Brain-Computer Interface. in 9th IEEE International Conference on Control and Automation, ICCA (2011).

DOI: 10.1109/icca.2011.6138097

Google Scholar

[6] Jiao, Y., X. Wu and X. Guo. Motor imagery classification based on the optimized SVM and BPNN by GA. in 2010 International Conference on Intelligent Control and Information Processing, ICICIP (2010).

DOI: 10.1109/icicip.2010.5564261

Google Scholar

[7] Siuly, S. and L. Yan, Improving the Separability of Motor Imagery EEG Signals Using a Cross Correlation-Based Least Square Support Vector Machine for Brain-Computer Interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2012. 20(4): pp.526-538.

DOI: 10.1109/tnsre.2012.2184838

Google Scholar

[8] Ramoser, H., J. Muller-Gerking and G. Pfurtscheller, Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Transactions on Rehabilitation Engineering, 2000. 8(4): pp.441-446.

DOI: 10.1109/86.895946

Google Scholar