EEG-based Motion Sickness Classification system with Genetic Feature Selection

Abstract

People tend to get motion sickness on a moving boat, train, airplane, car, or amusement park rides. Many previous studies indicated that motion sickness sometimes led to traffic accidents, so it becomes an important issue in our daily life. In this study, we designed a VIZ-based motion-sickness platform with a 32-channel EEG system and a joystick which is used to report the motion sickness level (MSL) in real time during experiments. The results show it is feasible to estimate subject's MSL based on re-sampling frequency band proved by the high test accuracy. A comparison between general prediction models (such as LDA, QDA, KNN) and IBCGA shows that the IBCGA can be effectively increase the accuracy. In this paper, an extended-IBCGA (e-IBCGA) is proposed and it provides more accuracy than the prior-art research. The test results show that e-IBCGA increases at least 10% to 20% test accuracy in 6 subjects.