標題: EEG-Based Learning System for Online Motion Sickness Level Estimation in a Dynamic Vehicle Environment
作者: Lin, Chin-Teng
Tsai, Shu-Fang
Ko, Li-Wei
生物資訊及系統生物研究所
資訊工程學系
腦科學研究中心
Institude of Bioinformatics and Systems Biology
Department of Computer Science
Brain Research Center
關鍵字: Driving cognition;electroencephalography (EEG);learning system;motion sickness;online estimation
公開日期: 1-十月-2013
摘要: Motion 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%.
URI: http://dx.doi.org/10.1109/TNNLS.2013.2275003
http://hdl.handle.net/11536/23008
ISSN: 2162-237X
DOI: 10.1109/TNNLS.2013.2275003
期刊: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume: 24
Issue: 10
起始頁: 1689
結束頁: 1700
顯示於類別:期刊論文


文件中的檔案:

  1. 000325981400015.pdf