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dc.contributor.authorChikara, Rupesh Kumaren_US
dc.contributor.authorKo, Li-Weien_US
dc.date.accessioned2017-04-21T06:48:22Z-
dc.date.available2017-04-21T06:48:22Z-
dc.date.issued2015en_US
dc.identifier.isbn978-1-4673-9606-6en_US
dc.identifier.urihttp://hdl.handle.net/11536/135997-
dc.description.abstractIn this paper, we present a classification method based on electroencephalogram (EEG) signal during left hand and right hand response inhibition (stop success vs stop fail) from different participants. The system uses phase locking value (PLV) for the features extraction and pattern recognition algorithm for classification. There are four classifiers: QDC, KNNC, PARZENDC and LDC used in this paper to estimate the accuracy of our system. Based on the collected time-domain EEG signals, the phase locking value (PLV) from C3-CZ and C4- CZ electrodes are calculated and then used as the feature and input for the classifiers algorithm. The classification system demonstrate an accuracy of 92 % in LDC. The results of this study suggest the method could be utilized effectively for response inhibition identification.en_US
dc.language.isoen_USen_US
dc.subjectEEGen_US
dc.subjectPLVen_US
dc.subjectBCIen_US
dc.subjectClassificationen_US
dc.subjectResponse Inhibitionen_US
dc.titleClassification of EEG-P300 Signals Using Phase Locking Value and Pattern Recognition Classifiersen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2015 CONFERENCE ON TECHNOLOGIES AND APPLICATIONS OF ARTIFICIAL INTELLIGENCE (TAAI)en_US
dc.citation.spage367en_US
dc.citation.epage372en_US
dc.contributor.department生物科技學系zh_TW
dc.contributor.department生物資訊及系統生物研究所zh_TW
dc.contributor.department腦科學研究中心zh_TW
dc.contributor.departmentDepartment of Biological Science and Technologyen_US
dc.contributor.departmentInstitude of Bioinformatics and Systems Biologyen_US
dc.contributor.departmentBrain Research Centeren_US
dc.identifier.wosnumberWOS:000380406200049en_US
dc.citation.woscount0en_US
Appears in Collections:Conferences Paper