Node-wise Domain Adaptation Based on Transferable Attention for Recognizing Road Rage via EEG
This addresses road rage detection, a social problem with limited prior research, using EEG data, but it is incremental as it builds on existing graph neural network and domain adaptation techniques.
The paper tackles road rage recognition from EEG signals by proposing a model combining transferable attention and regularized graph neural networks, achieving 85.63% accuracy in cross-subject experiments.
Road rage is a social problem that deserves attention, but little research has been done so far. In this paper, based on the biological topology of multi-channel EEG signals,we propose a model which combines transferable attention (TA) and regularized graph neural network (RGNN). First, topology-aware information aggregation is performed on EEG signals, and complex relationships between channels are dynamically learned. Then, the transferability of each channel is quantified based on the results of the node-wise domain classifier, which is used as attention score. We recruited 10 subjects and collected their EEG signals in pleasure and rage state in simulated driving conditions. We verify the effectiveness of our method on this dataset and compare it with other methods. The results indicate that our method is simple and efficient, with 85.63% accuracy in cross-subject experiments. It can be used to identify road rage. Our data and code are available. https://github.com/1CEc0ffee/dataAndCode.git