Int J Neural Syst - Efficient automatic selection and combination of EEG features in least squares classifiers for motor imagery brain-computer interfaces.

Tópicos

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Resumo

Discriminative features have to be properly extracted and selected from the electroencephalographic (EEG) signals of each specific subject in order to achieve an adaptive brain-computer interface (BCI) system. This work presents an efficient wrapper-based methodology for feature selection and least squares discrimination of high-dimensional EEG data with low computational complexity. Features are computed in different time segments using three widely used methods for motor imagery tasks and, then, they are concatenated or averaged in order to take into account the time course variability of the EEG signals. Once EEG features have been extracted, proposed framework comprises two stages. The first stage entails feature ranking and, in this work, two different procedures have been considered, the least angle regression (LARS) and the Wilcoxon rank sum test, to compare the performance of each one. The second stage selects the most relevant features using an efficient leave-one-out (LOO) estimation based on the Allen's PRESS statistic. Experimental comparisons with the state-of-the-art BCI methods shows that this approach gives better results than current state-of-the-art approaches in terms of recognition rates and computational requirements and, also with respect to the first ranking stage, it is confirmed that the LARS algorithm provides better results than the Wilcoxon rank sum test for these experiments.

Resumo Limpo

discrimin featur proper extract select electroencephalograph eeg signal specif subject order achiev adapt braincomput interfac bci system work present effici wrapperbas methodolog featur select least squar discrimin highdimension eeg data low comput complex featur comput differ time segment use three wide use method motor imageri task concaten averag order take account time cours variabl eeg signal eeg featur extract propos framework compris two stage first stage entail featur rank work two differ procedur consid least angl regress lar wilcoxon rank sum test compar perform one second stage select relev featur use effici leaveoneout loo estim base allen press statist experiment comparison stateoftheart bci method show approach give better result current stateoftheart approach term recognit rate comput requir also respect first rank stage confirm lar algorithm provid better result wilcoxon rank sum test experi

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