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dc.contributor.authorDuann, Jeng-Renen_US
dc.contributor.authorChen, Po-Chuanen_US
dc.contributor.authorKo, Li-Weien_US
dc.contributor.authorHuang, Ruey-Songen_US
dc.contributor.authorJung, Tzyy-Pingen_US
dc.contributor.authorLin, Chin-Tengen_US
dc.date.accessioned2017-04-21T06:49:42Z-
dc.date.available2017-04-21T06:49:42Z-
dc.date.issued2009en_US
dc.identifier.isbn978-3-642-02811-3en_US
dc.identifier.issn0302-9743en_US
dc.identifier.urihttp://hdl.handle.net/11536/134415-
dc.description.abstractThis study demonstrates the acquisitions of EEG signals from non-hairy forehead sites and tested the feasibility of using the forehead EEG in detecting drowsiness-related brain activities. A custom-made 15-channel forehead EEG-electrode patch and 28 scalp electrodes placed according to the International 10-20 system were used to simultaneously record EEG signals from the forehead and whole-head regions, respectively. A total of five Subjects were instructed to perform a night-time long-haul driving task for all hour in a virtual-reality based driving simulator comprising a real car mounted oil a 6 degree-of-freedom Steward motion platform and a immersive VR environment with 360 degree projection scenes. Separate independent component analyses were applied to the forehead and whole-head EEG data for each individual subject. For the whole-head independent component (IC) set, the frontal central midline (FCM) IC with an equivalent dipole source located in the interior cingulate cortex was selected for further analysis. For the forehead IC set, the IC with its theta power changes highly correlated with Subject\'s driving performance was selected. The EEG power changes of the selected forehead ICs were then used to predict driving performance based on a linear regression model. The results of this study showed that it is feasible to accurately estimate quantitatively the changing level of driving performance using the EEG features obtained from the forehead non-hairy channels, and the estimation accuracy was comparable to that using the EEG features of the whole-head recordings.en_US
dc.language.isoen_USen_US
dc.subjectForehead EEGen_US
dc.subjectDrowsinessen_US
dc.subjectDriving performanceen_US
dc.subjectIndependent component analysis (ICA)en_US
dc.titleDetecting Frontal EEG Activities with Forehead Electrodesen_US
dc.typeProceedings Paperen_US
dc.identifier.journalFOUNDATIONS OF AUGMENTED COGNITION, PROCEEDINGS: NEUROERGONOMICS AND OPERATIONAL NEUROSCIENCEen_US
dc.citation.volume5638en_US
dc.citation.spage373en_US
dc.citation.epage+en_US
dc.contributor.department腦科學研究中心zh_TW
dc.contributor.departmentBrain Research Centeren_US
dc.identifier.wosnumberWOS:000268101300044en_US
dc.citation.woscount0en_US
Appears in Collections:Conferences Paper