Abstract
There often exist redundant channels in EEG signal collection which deteriorate the classification accuracy. In this paper, a classification method which can deal with redundant channels, as well as redundant CSP features, is presented for motor imagery task. Our method utilizes CSP filter and margin maximization with linear programming to update a compound weight that enables iterative channel elimination and the update of the following linear classification. Theoretical analysis and experimental results show the effectiveness of our method to solve redundancy of channels and CSP features simultaneously when classifying motor imagery EEG data.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Wu, W., Gao, X.R., Hong, B., et al.: Classifying Single-Trail EEG During Motor Imagery by Iterative Spatio-Spectral Patterns Learning (ISSPL). IEEE Trans. on Biomedical Engineering 55(6), 1733–1743 (2008)
Lal, T.N., Schroder, M., Hinterberger, T., et al.: Support Vector Channel Selection in BCI. IEEE Trans. on Biomedical Engineering 51(6), 1003–1010 (2004)
Li, Y.Q., Guan, C.T.: Joint Feature Re-extraction and Classification Using an Iteration. Machine Learning 71, 33–53 (2008)
Li, Y.Q., Cichocki, A., Amari, S.: Analysis of Sparse Representation and Blind Source Separation. Neural Computation 16(6), 1193–1234 (2004)
Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge Press, London (2004)
Zhu, J., Rosset, S., Hastie, T., Tibshirani, R.: 1-norm Support Vector Machines. In: NIPS, pp. 49–56 (2004)
Guo, G.D., Dyer, C.R.: Simultaneous Feature Selection and Classifier Training via Linear Programming: A Case Study for Face Expression Recognition. In: CVPR, vol. 1, pp. 346–352 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
He, L., Gu, Z., Li, Y., Yu, Z. (2010). Classifying Motor Imagery EEG Signals by Iterative Channel Elimination according to Compound Weight. In: Wang, F.L., Deng, H., Gao, Y., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2010. Lecture Notes in Computer Science(), vol 6320. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16527-6_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-16527-6_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16526-9
Online ISBN: 978-3-642-16527-6
eBook Packages: Computer ScienceComputer Science (R0)