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.
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.