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dc.contributor.authorLin, Chin-Tengen_US
dc.contributor.authorTsai, Shu-Fangen_US
dc.contributor.authorKo, Li-Weien_US
dc.date.accessioned2014-12-08T15:33:03Z-
dc.date.available2014-12-08T15:33:03Z-
dc.date.issued2013-10-01en_US
dc.identifier.issn2162-237Xen_US
dc.identifier.urihttp://dx.doi.org/10.1109/TNNLS.2013.2275003en_US
dc.identifier.urihttp://hdl.handle.net/11536/23008-
dc.description.abstractMotion sickness is a common experience for many people. Several previous researches indicated that motion sickness has a negative effect on driving performance and sometimes leads to serious traffic accidents because of a decline in a person's ability to maintain self-control. This safety issue has motivated us to find a way to prevent vehicle accidents. Our target was to determine a set of valid motion sickness indicators that would predict the occurrence of a person's motion sickness as soon as possible. A successful method for the early detection of motion sickness will help us to construct a cognitive monitoring system. Such a monitoring system can alert people before they become sick and prevent them from being distracted by various motion sickness symptoms while driving or riding in a car. In our past researches, we investigated the physiological changes that occur during the transition of a passenger's cognitive state using electroencephalography (EEG) power spectrum analysis, and we found that the EEG power responses in the left and right motors, parietal, lateral occipital, and occipital midline brain areas were more highly correlated to subjective sickness levels than other brain areas. In this paper, we propose the use of a self-organizing neural fuzzy inference network (SONFIN) to estimate a driver's/passenger's sickness level based on EEG features that have been extracted online from five motion sickness-related brain areas, while either in real or virtual vehicle environments. The results show that our proposed learning system is capable of extracting a set of valid motion sickness indicators that originated from EEG dynamics, and through SONFIN, a neuro-fuzzy prediction model, we successfully translated the set of motion sickness indicators into motion sickness levels. The overall performance of this proposed EEG-based learning system can achieve an average prediction accuracy of similar to 82%.en_US
dc.language.isoen_USen_US
dc.subjectDriving cognitionen_US
dc.subjectelectroencephalography (EEG)en_US
dc.subjectlearning systemen_US
dc.subjectmotion sicknessen_US
dc.subjectonline estimationen_US
dc.titleEEG-Based Learning System for Online Motion Sickness Level Estimation in a Dynamic Vehicle Environmenten_US
dc.typeArticleen_US
dc.identifier.doi10.1109/TNNLS.2013.2275003en_US
dc.identifier.journalIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMSen_US
dc.citation.volume24en_US
dc.citation.issue10en_US
dc.citation.spage1689en_US
dc.citation.epage1700en_US
dc.contributor.department生物資訊及系統生物研究所zh_TW
dc.contributor.department資訊工程學系zh_TW
dc.contributor.department腦科學研究中心zh_TW
dc.contributor.departmentInstitude of Bioinformatics and Systems Biologyen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.contributor.departmentBrain Research Centeren_US
dc.identifier.wosnumberWOS:000325981400015-
dc.citation.woscount0-
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