Brain Signal-Based Motion Sickness Classification in Automobile Passengers

Abstract

Motion sickness remains a critical challenge for enhancing passenger comfort, particularly in autonomous vehicles, where non-driving activities are a primary benefit. This study investigates the multi-class classification of motion sickness levels using brain signals measured through 8-channel EEG and 1-channel fNIRS during general road driving scenarios, including motion sickness-inducing sections. A total of four deep learning models (CNN, LSTM, EEGNet, and Conformer) were employed, with the Conformer demonstrating superior performance. The results reveal that specific motion sickness levels, including the critical transition phase from mild to severe motion sickness, can be effectively identified using EEG and fNIRS data. EEG analysis highlighted distinct brain activation patterns across motion sickness levels, while fNIRS demonstrated higher classification accuracy due to its sensitivity to changes in cerebral blood flow caused by accelerations and decelerations. The combined use of EEG and fNIRS achieved the highest accuracy of 78.64%, demonstrating the synergistic potential of multi-modal data. © 2025 IEEE.