EEG feature selection using mutual information and support vector machine: A comparative analysis

Carlos Guerrero-Mosquera, Michel Verleysen, Angel Navia Vazquez

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

27 Cites (Scopus)

Resum

The large number of methods for EEG feature extraction demands a good choice for EEG features for every task. This paper compares three subsets of features obtained by tracks extraction method, wavelet transform and fractional Fourier transform. Particularly, we compare the performance of each subset in classification tasks using support vector machines and then we select possible combination of features b y feature selection methods based on forward-backward procedure and mutual information as relevance criteria. Results confirm that fractional Fourier transform coefficients present very good performance and also the possibility of using some combination of this features to improve the performance of the classifier. To reinforce the relevance of the study, we carry out 1000 independent runs using a bootstrap approach, and evaluate the statistical significance of the Fscore results using the Kruskal-Wallis test.

Idioma originalAnglès
Títol de la publicació2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
Pàgines4946-4949
Nombre de pàgines4
DOIs
Estat de la publicacióPublicada - 2010
Publicat externament
Esdeveniment2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 - Buenos Aires, Argentina
Durada: 31 d’ag. 20104 de set. 2010

Sèrie de publicacions

Nom2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10

Conferència

Conferència2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
País/TerritoriArgentina
CiutatBuenos Aires
Període31/08/104/09/10

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