论文标题
基于可转移注意的节点域的适应
Node-wise Domain Adaptation Based on Transferable Attention for Recognizing Road Rage via EEG
论文作者
论文摘要
道路愤怒是一个值得关注的社会问题，但到目前为止，几乎没有研究。在本文中，基于多通道EEG信号的生物学拓扑，我们提出了一个结合可转移注意力（TA）和正规图神经网络（RGNN）的模型。首先，在脑电图信号上执行拓扑感知信息聚合，并动态学习通道之间的复杂关系。然后，根据节点域分类器的结果来量化每个通道的可传递性，该域分类器被用作注意力评分。我们招募了10名受试者，并在模拟驾驶条件下以愉悦和愤怒的状态收集了他们的脑电图信号。我们在此数据集上验证方法的有效性，并将其与其他方法进行比较。结果表明我们的方法是简单有效的，在跨主题实验中，精度为85.63％。它可用于识别道路愤怒。我们的数据和代码可用。 https://github.com/1cec0ffee/dataandcode.git
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
