首页|Unraveling the Time-Frequency Features of Emotional Regulation: Based on an Inte rpretable XGBoost-SHAP Analytical Framework
Unraveling the Time-Frequency Features of Emotional Regulation: Based on an Inte rpretable XGBoost-SHAP Analytical Framework
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from bi orxiv.org: "Negative emotions, while crucial for survival, can lead to adverse health effec ts 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 techniqu es, XGBoost-SHAP model, to analyze EEG data. "This study investigates the neural mechanisms underlying emotion regulation, wi th 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 ide ntifying predictive features and patterns, this approach offers insights into th e temporal dynamics of emotion regulation and the involvement of specific brain regions, enhancing our understanding of emotional processing and providing avenu es for effective interventions. The findings reveal a significant relationship b etween specific EEG feature changes and emotional ratings during the emotion reg ulation process. The LPFC emerges as central in cognitive control and emotional regulation.
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