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

TL;DR

This paper introduces a novel EEG-based model combining transferable attention and graph neural networks to accurately identify road rage across subjects, demonstrating 85.63% accuracy.

Contribution

It proposes a new method leveraging topology-aware aggregation and node-wise domain adaptation for EEG-based emotion recognition in driving scenarios.

Findings

Achieved 85.63% accuracy in cross-subject road rage detection.

Demonstrated the effectiveness of topology-aware information aggregation.

Validated the method on a newly collected EEG dataset.

Abstract

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