Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cheng, Eric Juwei | en_US |
dc.contributor.author | Young, Ku-Young | en_US |
dc.contributor.author | Lin, Chin-Teng | en_US |
dc.date.accessioned | 2019-04-02T06:04:33Z | - |
dc.date.available | 2019-04-02T06:04:33Z | - |
dc.date.issued | 2018-01-01 | en_US |
dc.identifier.issn | 2473-7240 | en_US |
dc.identifier.uri | http://hdl.handle.net/11536/151007 | - |
dc.description.abstract | This study proposes a EEG-based prediction system that transform the measured EEG record into an image-liked data for estimating the drowsiness level of drivers. Drowsy driving is one of the main factors to the occurrence of traffic accident. Since drivers themselves may not always immediately recognize that they are in the drowsy state, the risk of traffic accident increases while the driver is in the low vigilance state. In order to address this problem, the estimation of drowsy driving state via brain-computer interfaces (BCI) becomes a major concern in the driving safety field. This study transforms the measured EEG record into a image-liked feature maps, and then passes these feature maps to a Convolutional Neural Network (CNN) to learn the discriminative representations. The proposed drowsiness prediction system is evaluated by leave-one-subject-out cross-validation. The results indicate that our approach provides impressive and robust prediction performance on the EEG dataset without artifact removal process. | en_US |
dc.language.iso | en_US | en_US |
dc.title | Image-based EEG signal processing for driving fatigue prediction | en_US |
dc.type | Proceedings Paper | en_US |
dc.identifier.journal | 2018 INTERNATIONAL AUTOMATIC CONTROL CONFERENCE (CACS) | en_US |
dc.contributor.department | 電子工程學系及電子研究所 | zh_TW |
dc.contributor.department | Department of Electronics Engineering and Institute of Electronics | en_US |
dc.identifier.wosnumber | WOS:000457604100004 | en_US |
dc.citation.woscount | 0 | en_US |
Appears in Collections: | Conferences Paper |