Emotion recognition plays a crucial role in human-computer interaction, and
electroencephalography (EEG) is advantageous for reflecting human emotional
states. In this study, we propose MACTN, a hierarchical hybrid model for
jointly modeling local and global temporal information. The model is inspired
by neuroscience research on the temporal dynamics of emotions. MACTN extracts
local emotional features through a convolutional neural network (CNN) and
integrates sparse global emotional features through a transformer. Moreover, we
employ channel attention mechanisms to identify the most task-relevant
channels. Through extensive experimentation on two publicly available datasets,
namely THU-EP and DEAP, our proposed method, MACTN, consistently achieves
superior classification accuracy and F1 scores compared to other existing
methods in most experimental settings. Furthermore, ablation studies have shown
that the integration of both self-attention mechanisms and channel attention
mechanisms leads to improved classification performance. Finally, an earlier
version of this method, which shares the same ideas, won the Emotional BCI
Competition's final championship in the 2022 World Robot Contest.

提出了一种基于分层混合模型的情感识别方法 MACTN，该模型通过卷积神经网络 (CNN) 提取局部情感特征，通过 transformer 集成稀疏的全局情感特征，并利用通道注意机制识别最相关的通道，在实验中取得超过其他现有方法的分类准确度和 F1 分数，早期版本已赢得 2022 年世界机器人大赛情感 BCI 竞赛的决赛冠军。