title: Combination of frequency bands in EEG for feature reduction in mental task classification.
abstract: Brain-computer interfaces require online processing of electroencephalogram (EEG) measurements. Therefore, speed of signal processing is of great importance in BCI systems. We present a method of feature reduction by combining frequency band powers of EEG, in order to speed up processing and meanwhile avoid classifier overfitting. As a result a linear combination of power spectrum of EEG frequency bands (alpha, beta, gamma, delta & theta) was found that reduces the dimension of feature vector by a factor of 5. This method gives a total correct classification rate of 91.71% comparing to 87.96% achieved from direct use of frequency band powers and 85.54% achieved from PCA feature reduction method applied to the same feature vector with 14 components.
authors.affiliation: Biomedical Engineering Faculty, Amirkabir University of Technol.ogy, Tehran, Iran. farnaz6112@yahoo.com
authors.forename: Farnaz
authors.initials: F
authors.lastname: Abdollahi
authors.forename: Ali
authors.initials: A
authors.lastname: Motie-Nasrabadi
mesh_terms: D000465:Algorithms; D003071:Cognition / Q000502:physiology*; D016002:Discriminant Analysis; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D005583:Fourier Analysis; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis