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|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.title||Imaginary hand movement classification using electroencephalography||en_US|
|article.title.sourcetitle||Proceedings - 7th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2017||en_US|
|article.stream.affiliations||Chiang Mai University||en_US|
|Appears in Collections:||CMUL: Journal Articles|
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