EEG Emotion Recognition Based on an Implicit Emotion Regulatory Mechanism

One of the main challenges in electroencephalography (EEG) emotion recognition is the lack of understanding of the biological properties of the brain and how they relate to emotions. To address this issue, this paper proposes an implicit emotion regulatory mechanism inspired contrastive learning framework (CLIER) for EEG emotion recognition. The framework simulates the complex relationship between emotions and the underlying neurobiological processes; to achieve this, the mechanism is mainly simulated through three parts. First, to leverage the inter-individual variability of emotional expression, the emotion features of the individual are captured by a dynamic connection graph in the subject-dependent setting. Subsequently, reverse regulation is simulated by contrast learning based on label information and data augmentation to capture more biologically specific emotional features. Finally, caused by the asymmetry between the left and right hemispheres of the human brain in response to emotions, brain lateralization mutual learning facilitates the fusion of the hemispheres in determining emotions. Experiments on SEED, SEED-IV, SEED-V, and EREMUS datasets show impressive results: 93.4% accuracy on SEED, 90.2% on SEED-IV, 82.46% on SEED-V, and 41.63% on EREMUS. Employing an identical experimental protocol, our model demonstrated superior performance relative to the majority of existing methods, thus showcasing its effectiveness in the realm of EEG emotion recognition.