Robust Supervised Graph Embedding Method For EEG-Based Brain Network Emotion Recognition

Abstract

Emotion recognition based on brain networks has attracted increasing research attention due to its ability to reveal the information interactions between brain regions under different emotional states. However, there are still two challenges in practical applications: 1) The high dimensionality of brain networks can lead to issues of feature redundancy, overfitting, and high computational costs; 2) Electroencephalography (EEG) signals are susceptible to outlier noise, and label noise caused by mismatches between the stimuli labels used to induce emotions and the individuals’ actual emotional responses can significantly impact emotion recognition. To address these challenges, we propose a supervised graph embedding algorithm based on the L1-norm space (L1-SGE). This method leverages the local structure and class information of the original data for discriminative subspace learning, achieving a low-dimensional representation of high-dimensional networks. Additionally, the constraints of the L1-norm space enable the method to effectively suppress outliers and label noise. The performance on publicly available emotional EEG databases has successfully validated the effectiveness of the proposed method in low-dimensional feature representation and noise suppression. Furthermore, this method not only offers a powerful tool for research in affective brain-computer interfaces but also provides a potential solution for pattern recognition tasks facing similar challenges in the field of artificial intelligence.