EEG-based evaluation system for motion sickness estimation

Chun-Shu Wei, Li-Wei Ko*, Shang Wen Chuang, Tzyy Ping Jung, Chin-Teng Lin

*此作品的通信作者

研究成果: Conference contribution同行評審

18 引文 斯高帕斯(Scopus)

摘要

Motion sickness is a common symptom which occurs when the brain receives conflicting sensory information. Although many motion sickness-related biomarkers have been identified, estimating humans' motion sickness level (MSL) remains a challenge in operational environments. Traditionally, questionnaire and physical check are the common ways to passively evaluate subject's sickness level. This study proposes a physiology-based estimation system that can automatically assess subject's motion-sickness level in operational environments. Our previous study showed that increases in self-reported MSL in a Virtual-reality based driving experiment on a motion platform were accompanied by elevated alpha (8-12Hz) power most prominently in the occipital midline electroencephalogram (EEG). This study explores the feasibility of an automatic MSL estimation based on spontaneous EEG spectrum. To this end, this study employed three different estimators: 1) Linear regression (LR), 2) Radial basis function neural network (RBFNN), and 3) Support vector regression (SVR). The results of this study showed that SVR outperformed LR and RBFNN in estimating MSL from EEG spectrum. The averaged accuracy of MSL estimation by SVR was 86.926.09% across 6 subjects. This demonstration could lead to a practical system for noninvasive monitoring of the motion sickness in real-world environments.

原文English
主出版物標題2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011
頁面100-103
頁數4
DOIs
出版狀態Published - 28 4月 2011
事件2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011 - Cancun, Mexico
持續時間: 27 4月 20111 5月 2011

出版系列

名字2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011

Conference

Conference2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011
國家/地區Mexico
城市Cancun
期間27/04/111/05/11

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