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Road rage is a social problem that deserves attention, but little researchhas been done so far. In this paper, based on the biological topology ofmulti-channel EEG signals,we propose a model which combines transferableattention (TA) and regularized graph neural network (RGNN). First,topology-aware information aggregation is performed on EEG signals, and complexrelationships between channels are dynamically learned. Then, thetransferability of each channel is quantified based on the results of thenode-wise domain classifier, which is used as attention score. We recruited 10subjects and collected their EEG signals in pleasure and rage state insimulated driving conditions. We verify the effectiveness of our method on thisdataset and compare it with other methods. The results indicate that our methodis simple and efficient, with 85.63% accuracy in cross-subject experiments. Itcan be used to identify road rage. Our data and code are available.https://github.com/1CEc0ffee/dataAndCode.git

Node-wise Domain Adaptation Based on Transferable Attention for Recognizing Road Rage via EEG