Multimodal Classification of Motion Sickness Using EEG, fNIRS, and IMU Signals

Abstract

Motion sickness is characterized by nausea, dizziness, and vomiting, often caused by sensory conflict during passive motion. This study addresses the limitations of existing single-modal approaches by using a multimodal classification framework that integrates electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), and inertial measurement unit (IMU) signals. Data from 12 participants were analyzed using a transformer-based model. The EEG + fNIRS model achieved the highest k-fold cross-validation accuracy (79.51%) and AUC (85.36%) but had limited leave-one-subject-out performance (<60%). Model interpretation identified EEG features, particularly from PO7, as the most critical, with IMU features such as Z-axis acceleration providing complementary information. While the approach demonstrates the potential of multimodal classification, challenges in intersubject generalization require further refinement. © 2025 IEEE.