Int J Neural Syst - Single-trial motor imagery classification using asymmetry ratio, phase relation, wavelet-based fractal, and their selected combination.

Tópicos

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Resumo

An electroencephalogram (EEG) analysis system is proposed for single-trial classification of motor imagery (MI) data in this study. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the system mainly consists of enhanced active segment selection, feature extraction, feature selection and classification. In addition to the original use of continuous wavelet transform (CWT) and Student's two-sample t-statistics, the 2D anisotropic Gaussian filter is proposed to further refine the selection of active segments. We then extract several features, including spectral power and asymmetry ratio, coherence and phase-locking value, and multiresolution fractal feature vector, for subsequent classification. Next, genetic algorithm (GA) is used to select features from the combination of above-mentioned features. Finally, support vector machine (SVM) is used for classification. Compared with "without enhanced active segment selection," several potential features and linear discriminant analysis (LDA) on MI data from two data sets for 10 subjects, the results indicate that the proposed method achieves 86.7% average classification accuracy, which is promising in BCI applications.

Resumo Limpo

electroencephalogram eeg analysi system propos singletri classif motor imageri mi data studi appli eventrel brain potenti erp data acquir sensorimotor cortic system main consist enhanc activ segment select featur extract featur select classif addit origin use continu wavelet transform cwt student twosampl tstatist d anisotrop gaussian filter propos refin select activ segment extract sever featur includ spectral power asymmetri ratio coher phaselock valu multiresolut fractal featur vector subsequ classif next genet algorithm ga use select featur combin abovement featur final support vector machin svm use classif compar without enhanc activ segment select sever potenti featur linear discrimin analysi lda mi data two data set subject result indic propos method achiev averag classif accuraci promis bci applic

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