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EEG Data Mining Using PCA

EEG Data Mining Using PCA

Lenka Lhotská, Vladimír Krajca, Jitka Mohylová, Svojmil Petránek, Václav Gerla
Copyright: © 2009 |Pages: 20
ISBN13: 9781605662183|ISBN10: 1605662186|ISBN13 Softcover: 9781616926007|EISBN13: 9781605662190
DOI: 10.4018/978-1-60566-218-3.ch008
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MLA

Lhotská, Lenka, et al. "EEG Data Mining Using PCA." Data Mining and Medical Knowledge Management: Cases and Applications, edited by Petr Berka, et al., IGI Global, 2009, pp. 161-180. https://doi.org/10.4018/978-1-60566-218-3.ch008

APA

Lhotská, L., Krajca, V., Mohylová, J., Petránek, S., & Gerla, V. (2009). EEG Data Mining Using PCA. In P. Berka, J. Rauch, & D. Zighed (Eds.), Data Mining and Medical Knowledge Management: Cases and Applications (pp. 161-180). IGI Global. https://doi.org/10.4018/978-1-60566-218-3.ch008

Chicago

Lhotská, Lenka, et al. "EEG Data Mining Using PCA." In Data Mining and Medical Knowledge Management: Cases and Applications, edited by Petr Berka, Jan Rauch, and Djamel Abdelkader Zighed, 161-180. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-218-3.ch008

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Abstract

This chapter deals with the application of principal components analysis (PCA) to the field of data mining in electroencephalogram (EEG) processing. The principal components are estimated from the signal by eigen decomposition of the covariance estimate of the input. Alternatively, they can be estimated by a neural network (NN) configured for extracting the first principal components. Instead of performing computationally complex operations for eigenvector estimation, the neural network can be trained to produce ordered first principal components. Possible applications include separation of different signal components for feature extraction in the field of EEG signal processing, adaptive segmentation, epileptic spike detection, and long-term EEG monitoring evaluation of patients in a coma.

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