Temporal Aware Mixed Attention-based Convolution and Transformer Network
  (MACTN) for EEG Emotion Recognition
Xiaopeng Si Dong Huang Yulin Sun Dong Ming
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.