Distinct neural correlates of active listening and passive listening to emotional narratives

In social communication, emotional responses and feedback often emerge from dynamic interactions, such as active listening. However, most previous research on electroencephalography (EEG)-based emotion recognition has relied on datasets collected from passive participants in controlled and isolated environments. This study addressed this gap by introducing a more naturalistic experimental design. We aimed at revealing the neural correlates of active and passive listening to emotional narratives, thereby simulating real-world social interactions. Using deep learning-based EEG emotion recognition, we employed a rhythm-specific convolutional neural network (CNN) combined with occlusion sensitivity analysis to investigate critical rhythmic and spatial information. Without prior feature engineering, the proposed approach achieved approximately 88% accuracy in distinguishing Happy/Sad from Neutral emotions. Our results highlighted the importance of gamma-band signals in emotion recognition, particularly in the left frontocentral and right temporal regions. We also identified the significant roles played by the left central-parietal, right parietal, and occipital regions during active listening to emotional narratives. This study demonstrates the feasibility of capturing essential rhythmic and spatial information through a rhythm-specific convolutional neural network combined with occlusion sensitivity analysis. This approach provides a robust foundation for uncovering the neural correlates of naturalistic emotional communication, paving the way for future research in social neuroscience.