Potential Indicator for Continuous Emotion Arousal by Dynamic Neural Synchrony

The need for automatic and high-quality emotion annotation is paramount in applications such as continuous emotion rec ognition and video highlight detection, yet achieving this through manu al human annotations is challenging. Inspired by inter-subject corre lation (ISC) utilized in neuroscience, this study introduces a novel Electr oencephalog-raphy (EEG) based ISC methodology that leverages a single-e lectrode and feature-based dynamic approach. Our contributions are three folds: Firstly, we reidentify two potent emotion features suitabl e for classifying emotions--first-order difference (FD) an differential entrop y (DE). Secondly, through the use of overall correlation analysis, we d emonstrate the heterogeneous synchronized performance of electrodes. Th is performance aligns with neural emotion patterns established in prior st udies, thus validating the effectiveness of our approach. Thirdly, by emplo ying a sliding window correlation technique, we showcase the significant c onsistency of dynamic ISCs across various features or key electrodes in ea ch analyzed film clip. Our findings indicate the method's reliability in c apturing consistent, dynamic shared neural synchrony among individual s, triggered by evocative film stimuli. This underscores the potential of our approach to serve as an indicator of continuous human emotion arousal . The implications of this research are significant for advancement s in affective computing and the broader neuroscience field, suggesting a s treamlined and effective tool for emotion analysis in real-world applic ations. 2 G. Pan et al.