Dimensionality Reduction For EEG Classification Using Mutual Information And SVM
C. Guerrero-Mosquera, M. Verleysen and Angel Navia-Vazquez
In: Proc. IEEE Int. Workshop on Machine Learning for Signal Processing MLSP11, September 18-21, 2011, Beijing, China.

Abstract

Dimensionality reduction is a well known technique in signal processing oriented to improve both the computational cost and the performance of classifiers. We use an electroencephalogram (EEG) feature matrix based on three extraction methods: tracks extraction, wavelets coefficients and Fractional Fourier Transform. The dimension reduction is performed by Mutual Information (MI) and a forwardbackward procedure. Our results show that feature extraction and dimension reduction could be considered as a new alternative for solving EEG classification problems.

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