Unraveling the Time-Frequency Features of Emotional Regulation: Based on an Interpretable XGBoost-SHAP Analytical Framework

Negative emotions, while crucial for survival, can lead to adverse health effects if not managed properly. Our understanding of temporal EEG changes during emotion regulation is limited. To address this gap, this study employs interpretable machine learning techniques, XGBoost-SHAP model, to analyze EEG data. This study investigates the neural mechanisms underlying emotion regulation, with a focus on EEG oscillations in the lateral prefrontal area channels (F3, F4, F7, F8) across four specific frequency bands (Alpha, Beta, Theta, Delta). By identifying predictive features and patterns, this approach offers insights into the temporal dynamics of emotion regulation and the involvement of specific brain regions, enhancing our understanding of emotional processing and providing avenues for effective interventions. The findings reveal a significant relationship between specific EEG feature changes and emotional ratings during the emotion regulation process. The LPFC emerges as central in cognitive control and emotional regulation. These results highlight the LPFCs rapid and effective role in regulating complex emotional dynamics, crucial for understanding and treating emotional disorders. The study underscores the importance of machine learning in elucidating neural mechanisms and guiding personalized interventions for emotional well-being.