Integrating prior knowledge of neurophysiology into neural network
architecture enhances the performance of emotion decoding. While numerous
techniques emphasize learning spatial and short-term temporal patterns, there
has been limited emphasis on capturing the vital long-term contextual
information associated with emotional cognitive processes. In order to address
this discrepancy, we introduce a novel transformer model called emotion
transformer (EmT). EmT is designed to excel in both generalized cross-subject
EEG emotion classification and regression tasks. In EmT, EEG signals are
transformed into a temporal graph format, creating a sequence of EEG feature
graphs using a temporal graph construction module (TGC). A novel residual
multi-view pyramid GCN module (RMPG) is then proposed to learn dynamic graph
representations for each EEG feature graph within the series, and the learned
representations of each graph are fused into one token. Furthermore, we design
a temporal contextual transformer module (TCT) with two types of token mixers
to learn the temporal contextual information. Finally, the task-specific output
module (TSO) generates the desired outputs. Experiments on four publicly
available datasets show that EmT achieves higher results than the baseline
methods for both EEG emotion classification and regression tasks. The code is
available at this https URL

将神经生理学的先前知识融入神经网络结构可提高情绪解码的性能。通过引入一种称为 EmT 的新型变压器模型，将 EEG 信号转换为时态图形式，并设计了一种用于学习时态上下文信息的模块，EmT 在广义跨受试者 EEG 情绪分类和回归任务中表现出色。