Enhancing local representation learning through global-local integration with functional connectivity for EEG-based emotion recognition.

Emotion recognition based on electroencephalogram (EEG) signals is crucial in understanding human affective states. Current research has limitations in extracting local features. The representation capabilities of local features are limited, making it difficult to comprehensively capture emotional information.