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dc.contributor.authorHajinoroozi, Mehdien_US
dc.contributor.authorJung, Tzyy-Pingen_US
dc.contributor.authorLin, Chin-Tengen_US
dc.contributor.authorHuang, Yufeien_US
dc.date.accessioned2017-04-21T06:48:24Z-
dc.date.available2017-04-21T06:48:24Z-
dc.date.issued2015en_US
dc.identifier.isbn978-1-4799-1948-2en_US
dc.identifier.urihttp://hdl.handle.net/11536/136017-
dc.description.abstractThis study considers the prediction of driver\'s cognitive states from electroencephalographic (EEG) data. Extracting EEG features correlated with driver\'s cognitive states is key for achieving accurate prediction. However, high dimensionality and temporal-and-spatial correlations of EEG data make extraction of effective features difficult. This study explores the approaches based on deep belief networks (DBN) for feature extraction and dimension reduction. Experimental results of this study showed that DBN applied to channel epochs (DBN-C) produces the most discriminant features and the best classification performance is achieved when DBN-C is applied to the time-frequency and independent-component analysis transformed EEG data. The results suggested that DBN-C is a promising new method for extracting complex, discriminant features for EEG-based brain computer interfaces.en_US
dc.language.isoen_USen_US
dc.subjectDeep belief networken_US
dc.subjectfeature extractio Classificationen_US
dc.titleFEATURE EXTRACTION WITH DEEP BELIEF NETWORKS FOR DRIVER\'S COGNITIVE STATES PREDICTION FROM EEG DATAen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2015 IEEE CHINA SUMMIT & INTERNATIONAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSINGen_US
dc.citation.spage812en_US
dc.citation.epage815en_US
dc.contributor.department腦科學研究中心zh_TW
dc.contributor.departmentBrain Research Centeren_US
dc.identifier.wosnumberWOS:000380436500167en_US
dc.citation.woscount1en_US
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