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Emotion recognition plays a crucial role in human-computer interaction, andelectroencephalography (EEG) is advantageous for reflecting human emotionalstates. In this study, we propose MACTN, a hierarchical hybrid model forjointly modeling local and global temporal information. The model is inspiredby neuroscience research on the temporal dynamics of emotions. MACTN extractslocal emotional features through a convolutional neural network (CNN) andintegrates sparse global emotional features through a transformer. Moreover, weemploy channel attention mechanisms to identify the most task-relevantchannels. Through extensive experimentation on two publicly available datasets,namely THU-EP and DEAP, our proposed method, MACTN, consistently achievessuperior classification accuracy and F1 scores compared to other existingmethods in most experimental settings. Furthermore, ablation studies have shownthat the integration of both self-attention mechanisms and channel attentionmechanisms leads to improved classification performance. Finally, an earlierversion of this method, which shares the same ideas, won the Emotional BCICompetition's final championship in the 2022 World Robot Contest.

Temporal Aware Mixed Attention-based Convolution and Transformer Network (MACTN) for EEG Emotion Recognition