Motion Sickness Prediction Based on Dry EEG in Real Driving Environment

Currently, the expectations for autonomous vehicles (AVs) are increasing. However, it is expected to take at least a decade to develop a fully AV, where human intervention is completely unrequired. By then, human driving is required if necessary. Currently, when the AV hands over control to the driver, a safe driving environment can be created only if it is possible to determine whether the driver is in an abnormal state. Unfortunately, according to the sensory conflict theory, the risk of motion sickness (MS) is higher in AV than in ordinary vehicles. This is because neither passengers nor drivers can predict the movement path of the vehicle under AV, so there is more dissonance between vision and perception. Because the technology to remove MS when it occurs has not yet been developed, the best way to maintain the driver's good condition is to quickly predict MS through the driver's bio-signals and establish a system to prevent MS through advanced driver assistance systems. It is necessary to quickly predict early MS and provide feedback before it becomes severe. In this study, we collected dry electroencephalogram (EEG) data to predict MS in a real-world driving environment. For MS-based feature extraction, a normalized sample covariance matrix-based feature representation method was used, and they were classified using convolutional neural networks. As a result, we achieved 89.05% (±5.76) accuracy when averaging all four experimental sessions we conducted. We expect our proposed model to be a useful indicator for resolving MS issues in AV environments.