Why the study?
Motion sickness is a common issue in electric vehicles that significantly impacts passenger comfort, prompting the need for a functional brain network model to evaluate symptoms.
Does integrating EEG and fNIRS data using a graph convolutional network improve motion sickness detection in electric vehicle passengers compared to single-modality approaches?
Does integrating EEG and fNIRS data using a graph convolutional network improve motion sickness detection in electric vehicle passengers compared to single-modality approaches?
Integrating EEG and fNIRS signals using a graph convolutional network significantly improves the detection of motion sickness in electric vehicles compared to single-modality approaches.
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Multimodal EEG-fNIRS fusion may aid EV passenger monitoring; leaves open larger validation before any design or clinical adoption.
Motion sickness is a common issue in electric vehicles, significantly impacting passenger comfort. This study aims to develop a functional brain network analysis model by integrating electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals to evaluate motion sickness symptoms. During real-world testing with the Feifan F7 series of new energy-electric vehicles from SAIC Motor Corp, data were collected from 32 participants. The EEG signals were divided into four frequency bands: delta-range, theta-range, alpha-range, and beta-range, and brain oxygenation variation was calculated from the fNIRS signals. Functional connectivity between brain regions was measured to construct functional brain network models for motion sickness analysis. A motion sickness detection model was developed using a graph convolutional network (GCN) to integrate EEG and fNIRS data. Our results show significant differences in brain functional connectivity between participants in motion and non-motion sickness states. The model that combined fNIRS data with high-frequency EEG signals achieved the best performance, improving the F1 score by 11.4% compared to using EEG data alone and by 8.2% compared to using fNIRS data alone. These results highlight the effectiveness of integrating EEG and fNIRS signals using GCN for motion sickness detection. They demonstrate the model's superiority over single-modality approaches, showcasing its potential for real-world applications in electric vehicles.
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Ren et al. (2024) studied this question.
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