EEG Correlation Analysis-guided Graph Local Enhanced Feature Learning For Emotion Recognition

Abstract

EEG-based emotion recognition is a key technology in brain-computer interfaces. Many previous studies have applied deep learning methods to mine emotion-related features in EEG to decode emotions. However, they overlooked the importance of electrode correlations and varying brain region activation during emotional processes, which are critical for emotion recognition. In this paper, we propose a method named EEG correlation analysis-guided graph local enhanced feature learning network (CAGLE-net). In CAGLE-net, we use the correlation analysis to guide the learning of the dynamic directed connection matrix to capture topological features, which are then fed into the locally enhanced embedding layer to generate enhanced features for each brain region. Subsequently, the cross-attention fusion mechanism is employed to fully leverage these locally enhanced features, yielding more discriminative representations. Experiment results on the SEED dataset show that CAGLE-net outperforms existing baseline methods. This study offers a promising solution for EEG-based emotion recognition.