CIT-EmotionNet: CNN Interactive Transformer Network for EEG Emotion Recognition
This work addresses emotion recognition from EEG signals for affective computing and intelligent interaction, representing an incremental improvement in combining features.
The paper tackled the problem of EEG emotion recognition by proposing CIT-EmotionNet, a CNN Interactive Transformer Network that integrates global and local features, achieving average recognition accuracies of 98.57% on SEED and 92.09% on SEED-IV datasets.
Emotion recognition using Electroencephalogram (EEG) signals has emerged as a significant research challenge in affective computing and intelligent interaction. However, effectively combining global and local features of EEG signals to improve performance in emotion recognition is still a difficult task. In this study, we propose a novel CNN Interactive Transformer Network for EEG Emotion Recognition, known as CIT-EmotionNet, which efficiently integrates global and local features of EEG signals. Initially, we convert raw EEG signals into spatial-frequency representations, which serve as inputs. Then, we integrate Convolutional Neural Network (CNN) and Transformer within a single framework in a parallel manner. Finally, we design a CNN interactive Transformer module, which facilitates the interaction and fusion of local and global features, thereby enhancing the model's ability to extract both types of features from EEG spatial-frequency representations. The proposed CIT-EmotionNet outperforms state-of-the-art methods, achieving an average recognition accuracy of 98.57\% and 92.09\% on two publicly available datasets, SEED and SEED-IV, respectively.