TY - GEN
T1 - Dimensionality reduction for EEG classification using mutual information and SVM
AU - Guerrero-Mosquera, Carlos
AU - Verleysen, Michel
AU - Navia Vazquez, Angel
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=82455212518&partnerID=8YFLogxK
U2 - 10.1109/MLSP.2011.6064595
DO - 10.1109/MLSP.2011.6064595
M3 - Conference contribution
AN - SCOPUS:82455212518
SN - 9781457716232
T3 - IEEE International Workshop on Machine Learning for Signal Processing
BT - 2011 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2011
T2 - 21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011
Y2 - 18 September 2011 through 21 September 2011
ER -