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dc.contributor.authorPornwitcha Somsapen_US
dc.contributor.authorNipon Theera-Umponen_US
dc.contributor.authorSansanee Auephanwiriyakulen_US
dc.description.abstract© 2017 IEEE. This paper proposes a method to help patients who cannot control their appendicular organs to communicate and to control devices via a binary decision by electroencephalography (EEG). We exploited 12 volunteers' EEG datasets from PhysioNet (EEG Motor Movement/Imaginary Datasets) that contain imaginary hand movement. For the signal selection, we have selected theta and alpha bands (4-15 Hz), since the signals in these bands are distinctively changed by the imagination. For the method, we have applied power spectrum density estimated by the autoregressive model (AR-model) to extract features, and then used principal component analysis (PCA) in order to reduce those features before the classification step. To measure the quality of the derived features, we used a set of classifiers including the decision tree, K-nearest neighborhood, and ensemble classifier. For the experiment, we conducted both intra-user and inter-user approaches. The leave-one-out cross validation was applied in the intra-user experiment while the five-fold cross validation was applied in the inter-user experiment. The results show that the highest average of classification accuracy is achieved by the cubic K-NN (97.08%) in the inter-user experiment and by the weighted K-NN (91.88%) in intra-user experiment.en_US
dc.subjectChemical Engineeringen_US
dc.subjectComputer Scienceen_US
dc.titleImaginary hand movement classification using electroencephalographyen_US
dc.typeConference Proceedingen_US
article.title.sourcetitleProceedings - 7th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2017en_US
article.volume2017-Novemberen_US Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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