Int J Neural Syst - Application of quantum-behaved particle swarm optimization to motor imagery EEG classification.

Tópicos

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Resumo

In this study, we propose a recognition system for single-trial analysis of motor imagery (MI) electroencephalogram (EEG) data. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the system chiefly consists of automatic artifact elimination, feature extraction, feature selection and classification. In addition to the use of independent component analysis, a similarity measure is proposed to further remove the electrooculographic (EOG) artifacts automatically. Several potential features, such as wavelet-fractal features, are then extracted for subsequent classification. Next, quantum-behaved particle swarm optimization (QPSO) is used to select features from the feature combination. Finally, selected sub-features are classified by support vector machine (SVM). Compared with without artifact elimination, feature selection using a genetic algorithm (GA) and feature classification with Fisher's linear discriminant (FLD) on MI data from two data sets for eight subjects, the results indicate that the proposed method is promising in brain-computer interface (BCI) applications.

Resumo Limpo

studi propos recognit system singletri analysi motor imageri mi electroencephalogram eeg data appli eventrel brain potenti erp data acquir sensorimotor cortic system chiefli consist automat artifact elimin featur extract featur select classif addit use independ compon analysi similar measur propos remov electrooculograph eog artifact automat sever potenti featur waveletfract featur extract subsequ classif next quantumbehav particl swarm optim qpso use select featur featur combin final select subfeatur classifi support vector machin svm compar without artifact elimin featur select use genet algorithm ga featur classif fisher linear discrimin fld mi data two data set eight subject result indic propos method promis braincomput interfac bci applic

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