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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chin-Teng Lin Shu-Fang Tsai Hua-Chin Lee Hui-Lin Huang Shinn-Ying Ho Li-Wei Ko |
| Copyright Year | 2012 |
| Description | Author affiliation: Institute of Bioinformatics and Systems Biology and Brain Research Center, National Chiao Tung University, 1001 Ta Hsueh Rd., Hsinchu 300, Taiwan (Li-Wei Ko) || Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan (Hua-Chin Lee; Hui-Lin Huang; Shinn-Ying Ho) || Institute of Computer Science and Engineering and Brain Research Center, National Chiao Tung University, 1001 Ta Hsueh Rd., Hsinchu 300, Taiwan (Chin-Teng Lin; Shu-Fang Tsai) |
| Abstract | Motion sickness occurs when the brain receives conflicting sensory information from body, inner ear and eyes [1]. In some cases, a decreased ability to actively control the body's postural motion also causes motion sickness [2][3]. Many previous studies have indicated that motion sickness had negative effect on driving performance, and sometimes lead to serious traffic accidents due to self-control ability decline. Therefore motion sickness becomes a very important issue in our daily life especially considering driving safety. There are many attempts made by researchers to realize motion sickness, and detect motion sickness in the early stage. Although many motion-sickness-related biomarkers have been identified, estimating human motion sickness level (MSL) remains a challenge in operational environment. In our past studies, we found that features in the occipital area were highly correlated with the driver's driving performance. In this study, we designed a virtual-reality (VR) based driving environment with instinct-MSL-reporting mechanism. When a subject performed a driving task, his/her brain EEG was recorded simultaneously. From those EEG data, features associated with left motor brain area, parietal brain area and occipital midline brain area which predicted MSL were extracted by an optimal classifier implemented by an inheritable bi-objective combinatorial genetic algorithm (IBCGA) with support vector machine. Unlike traditional correlation-based method, IBCGA aims to select a small set of EEG features and maximize the prediction accuracy simultaneously in BCI applications. Once the optimal feature set predicting MSL is successfully found, a driver's cognitive state can be monitored. |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 993383 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781467314886 |
| ISSN | 21614393 |
| e-ISBN | 9781467314909 |
| e-ISBN | 9781467314893 |
| DOI | 10.1109/IJCNN.2012.6252580 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-06-10 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Electroencephalography Support vector machines Accuracy Brain Estimation Visualization Roads |
| Content Type | Text |
| Resource Type | Article |
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