An innovative brain-inspired neuronal competition model for electroencephalography emotion recognition.

Objective: In the field of digital health, emotion recognition is vital for human-computer interaction and clinical neuropsychology. However, existing deep-learning models often lack interpretability, limiting their ability to provide insights into brain activity. Methods: This study proposes an innovative brain-inspired neuronal competition model for emotion recognition from electroencephalography (EEG). First, a discrete wavelet transform is employed to extract five brain rhythms (delta, theta, alpha, beta, and gamma) as feature inputs, maximizing the preservation of signal information. Then, the proposed model simulates excitatory and inhibitory competitive dynamics between neurons, where a rhythm attention encoder dynamically focuses on key brain rhythms and uses a leaky integrate-and-fire model to simulate the neuronal decision-making process. Finally, emotion recognition is implemented based on a winner-take-all principle. Results: Ten-fold cross-validation experiments on three widely used datasets, DEAP, SEED, and DREAMER, achieve average accuracies of 86.64-92.70% on DEAP, 81.89% on SEED, and 80.38-82.70% on DREAMER, respectively. These results indicate that different brain rhythms exhibit distinct characteristics in various emotion recognition tasks. Conclusions: This study presents a biologically plausible paradigm for EEG emotion recognition, laying the foundation for understanding the neurophysiological basis of brain activity and developing reliable tools for healthcare applications, such as mental health monitoring and depression detection.