  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.
