Electroencephalography (EEG), a technique that records electrical activity from the scalp using electrodes, plays a vital role in affective computing. However, fully utilizing the multi-domain characteristics of EEG signals remains a significant challenge. Traditional single-perspective analyses often fail to capture the complex interplay of temporal, frequency, and spatial dimensions in EEG data. To address this, we introduce a multi-view graph transformer (MVGT) based on spatial relations that integrates information across three domains: temporal dynamics from continuous series, frequency features extracted from frequency bands, and inter-channel relationships captured through several spatial encodings. This comprehensive approach allows model to capture the nuanced properties inherent in EEG signals, enhancing its flexibility and representational power. Evaluation on publicly available datasets demonstrates that MVGT surpasses state-of-the-art methods in performance. The results highlight its ability to extract multi-domain information and effectively model inter-channel relationships, showcasing its potential for EEG-based emotion recognition tasks.

翻译：脑电图（EEG）是一种利用电极记录头皮电活动的技术，在情感计算中发挥着重要作用。然而，充分利用EEG信号的多域特性仍然是一个重大挑战。传统单视角分析往往无法捕捉EEG数据中时间、频率和空间维度的复杂交互。为解决此问题，我们提出了一种基于空间关系的多视图图变换器（MVGT），该模型整合了三个域的信息：来自连续序列的时间动态特征、从频带中提取的频率特征，以及通过多种空间编码捕获的通道间关系。这种综合方法使模型能够捕捉EEG信号固有的细微属性，增强其灵活性和表示能力。在公开数据集上的评估表明，MVGT在性能上超越了现有最先进方法。结果凸显了其提取多域信息并有效建模通道间关系的能力，展示了其在基于EEG的情感识别任务中的潜力。