Dimensionality reduction for EEG classification using mutual information and SVM

Carlos Guerrero-Mosquera, Michel Verleysen, Angel Navia Vazquez

Producció científica: Capítol de llibreContribució a congrés/conferènciaAvaluat per experts

6 Cites (Scopus)

Resum

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 forward-backward procedure. Our results show that feature extraction and dimension reduction could be considered as a new alternative for solving EEG classification problems.

Idioma originalAnglès
Títol de la publicació2011 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2011
DOIs
Estat de la publicacióPublicada - 2011
Publicat externament
Esdeveniment21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011 - Beijing, China
Durada: 18 de set. 201121 de set. 2011

Sèrie de publicacions

NomIEEE International Workshop on Machine Learning for Signal Processing

Conferència

Conferència21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011
País/TerritoriChina
CiutatBeijing
Període18/09/1121/09/11

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