J Med Syst - Automatic and adaptive classification of electroencephalographic signals for brain computer interfaces.

Tópicos

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Resumo

Extracting knowledge from electroencephalographic (EEG) signals has become an increasingly important research area in biomedical engineering. In addition to its clinical diagnostic purposes, in recent years there have been many efforts to develop brain computer interface (BCI) systems, which allow users to control external devices only by using their brain activity. Once the EEG signals have been acquired, it is necessary to use appropriate feature extraction and classification methods adapted to the user in order to improve the performance of the BCI system and, also, to make its design stage easier. This work introduces a novel fast adaptive BCI system for automatic feature extraction and classification of EEG signals. The proposed system efficiently combines several well-known feature extraction procedures and automatically chooses the most useful features for performing the classification task. Three different feature extraction techniques are applied: power spectral density, Hjorth parameters and autoregressive modelling. The most relevant features for linear discrimination are selected using a fast and robust wrapper methodology. The proposed method is evaluated using EEG signals from nine subjects during motor imagery tasks. Obtained experimental results show its advantages over the state-of-the-art methods, especially in terms of classification accuracy and computational cost.

Resumo Limpo

extract knowledg electroencephalograph eeg signal becom increas import research area biomed engin addit clinic diagnost purpos recent year mani effort develop brain comput interfac bci system allow user control extern devic use brain activ eeg signal acquir necessari use appropri featur extract classif method adapt user order improv perform bci system also make design stage easier work introduc novel fast adapt bci system automat featur extract classif eeg signal propos system effici combin sever wellknown featur extract procedur automat choos use featur perform classif task three differ featur extract techniqu appli power spectral densiti hjorth paramet autoregress model relev featur linear discrimin select use fast robust wrapper methodolog propos method evalu use eeg signal nine subject motor imageri task obtain experiment result show advantag stateoftheart method especi term classif accuraci comput cost

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