  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
