MMPHGCN: A Hypergraph Convolutional Network for Detection of Driver Intention on Multimodal Physiological Signals

Detecting driving behavior is crucial as it is closely related to driving safety. With the development of electroencephalogram (EEG) signal processing, researchers have started studying driving intentions through EEG signals. However, current research mainly focuses on a single intention, and EEG signals suffer from low spatial resolution, leading to less accurate detection. This paper aims to investigate a set of driving intentions, targeting a classification task based on multimodal physiological signals. We propose a model that incorporates hypergraph convolution for feature extraction. Experimental results demonstrate that our approach outperforms the baselines in detecting various types of driving intentions with 74.40% accuracy, 5.89% higher than the best baseline. This research contributes to both traffic safety and brain-computer interfaces.