DIMENSIONALITY REDUCTION FOR EEG CLASSIFICATION USING MUTUAL INFORMATION AND SVM

被引：0

作者：

Guerrero-Mosquera, Carlos ^{[1
]}

Verleysen, Michel ^{[2
]}

Navia Vazquez, Angel ^{[1
]}

机构：

[1] Univ Carlos III Madrid, Signal Proc & Commun Dept, Avda Univ 30, Leganes 28911, Spain

[2] Catholic Univ Louvain, Machine Learning Grp, B-1348 Louvain, Belgium

来源：

2011 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP) | 2011年

关键词：

FEATURE-EXTRACTION;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页数：6

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