IEEE Trans Neural Netw Learn Syst. 2013 Oct;24(10):1689-700. doi: 10.1109/TNNLS.2013.2275003.
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 ~82%.
晕动病是许多人的常见体验。几项先前的研究表明，晕动病会对驾驶性能产生负面影响，有时会导致严重的交通事故，因为人的自我控制能力下降。这个安全问题促使我们寻找一种预防车辆事故的方法。我们的目标是确定一组有效的晕动病指标，以便尽快预测一个人是否会出现晕动病。成功的早期晕动病检测方法将有助于我们构建认知监测系统。这种监测系统可以在人们生病之前发出警报，防止他们在开车或乘车时因各种晕动病症状而分心。在过去的研究中，我们使用脑电图（EEG）功率谱分析研究了乘客认知状态转变期间发生的生理变化，我们发现左右电机、顶叶、外侧枕叶和枕中线上的 EEG 功率响应与主观疾病水平的相关性高于其他脑区。在本文中，我们提出使用自组织神经模糊推理网络（SONFIN）根据在线从五个与晕动病相关的大脑区域提取的 EEG 特征来估计驾驶员/乘客的疾病水平，无论是在真实还是虚拟的车辆环境中。结果表明，我们提出的学习系统能够提取一组源自 EEG 动力学的有效晕动病指标，并通过 SONFIN 神经模糊预测模型，成功地将这组晕动病指标转化为晕动病水平。该基于 EEG 的学习系统的整体性能平均预测准确率约为 82%。
