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DC Field | Value | Language |
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dc.contributor.author | Pornwitcha Somsap | en_US |
dc.contributor.author | Nipon Theera-Umpon | en_US |
dc.contributor.author | Sansanee Auephanwiriyakul | en_US |
dc.date.accessioned | 2018-09-05T04:23:42Z | - |
dc.date.available | 2018-09-05T04:23:42Z | - |
dc.date.issued | 2018-02-07 | en_US |
dc.identifier.other | 2-s2.0-85050368521 | en_US |
dc.identifier.other | 10.1109/ICCSCE.2017.8284416 | en_US |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85050368521&origin=inward | en_US |
dc.identifier.uri | http://cmuir.cmu.ac.th/jspui/handle/6653943832/58407 | - |
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.subject | Chemical Engineering | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Engineering | en_US |
dc.subject | Mathematics | en_US |
dc.title | Imaginary hand movement classification using electroencephalography | en_US |
dc.type | Conference Proceeding | en_US |
article.title.sourcetitle | Proceedings - 7th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2017 | en_US |
article.volume | 2017-November | en_US |
article.stream.affiliations | Chiang Mai University | en_US |
Appears in Collections: | CMUL: Journal Articles |
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