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EEG Data Mining Using PCA
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Author(s): Lenka Lhotská (Czech Technical University in Prague, Czech Republic), Vladimír Krajca (Faculty Hospital Na Bulovce, Czech Republic), Jitka Mohylová (Technical University Ostrava, Czech Republic), Svojmil Petránek (Faculty Hospital Na Bulovce, Czech Republic)and Václav Gerla (Czech Technical University in Prague, Czech Republic)
Copyright: 2009
Pages: 20
Source title:
Data Mining and Medical Knowledge Management: Cases and Applications
Source Author(s)/Editor(s): Petr Berka (University of Economics, Prague, Czech Republic), Jan Rauch (University of Economics, Prague, Czech Republic)and Djamel Abdelkader Zighed (University of Lumiere Lyon 2, France)
DOI: 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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