CIT-EmotionNet: CNN Interactive Transformer Network for EEG Emotion Recognition

• URL: http://arxiv.org/abs/2305.05548v1
• Date: Sun, 7 May 2023 16:27:09 GMT
• Title: CIT-EmotionNet: CNN Interactive Transformer Network for EEG Emotion Recognition
• Authors: Wei Lu, Hua Ma, and Tien-Ping Tan
• Abstract summary: We propose a novel CNN Interactive Transformer Network for EEG Emotion Recognition, known as CIT-EmotionNet. 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. 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.
• Score: 6.208851183775046
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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