变压器
脑电图
语音识别
计算机科学
主题(文档)
人工智能
模式识别(心理学)
心理学
工程类
神经科学
电气工程
电压
万维网
作者
Yi Ding,Chengxuan Tong,Shuailei Zhang,Muyun Jiang,Yong Li,Kevin Lim,Cuntai Guan
标识
DOI:10.1109/tnnls.2025.3552603
摘要
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 a 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 electroencephalography (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 (TGC) module. A novel residual multiview pyramid graph convolutional neural network (RMPG) module 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 (TCT) module with two types of token mixers to learn the temporal contextual information. Finally, the task-specific output (TSO) module 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 https://github.com/yi-ding-cs/EmT.
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