Electroencephalography (EEG), a medical imaging technique that captures scalp electrical activity of brain structures via electrodes, has been widely used in affective computing. The spatial domain of EEG is rich in affective information. However, few of the existing studies have simultaneously analyzed EEG signals from multiple perspectives of geometric and anatomical structures in spatial domain. In this paper, we propose a multi-view Graph Transformer (MVGT) based on spatial relations, which integrates information from the temporal, frequency and spatial domains, including geometric and anatomical structures, so as to enhance the expressive power of the model comprehensively. We incorporate the spatial information of EEG channels into the model as encoding, thereby improving its ability to perceive the spatial structure of the channels. Meanwhile, experimental results based on publicly available datasets demonstrate that our proposed model outperforms state-of-the-art methods in recent years. In addition, the results also show that the MVGT could extract information from multiple domains and capture inter-channel relationships in EEG emotion recognition tasks effectively.

翻译：脑电图（EEG）是一种通过电极捕获大脑结构头皮电活动的医学成像技术，已广泛应用于情感计算。EEG的空间域富含情感信息。然而，现有研究很少同时从空间域的几何结构和解剖结构等多个视角分析EEG信号。本文提出了一种基于空间关系的多视图图Transformer（MVGT），它整合了来自时域、频域和空间域（包括几何结构和解剖结构）的信息，从而全面增强模型的表达能力。我们将EEG通道的空间信息作为编码融入模型，从而提升其对通道空间结构的感知能力。同时，基于公开数据集的实验结果表明，我们提出的模型优于近年来的最先进方法。此外，结果还表明，MVGT能够从多个域中提取信息，并在EEG情绪识别任务中有效捕捉通道间关系。