面向标注高效的连续情绪唤醒度量化：基于组水平脑电动态神经同步性
Toward Annotation-Efficient Continuous Emotion Arousal Quantification via Group-Level EEG Dynamic Neural Synchrony
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中文总结 AI 辅助
该研究针对连续情绪唤醒度量化的手动标注瓶颈，提出用组水平EEG动态神经同步性（DNS）作为替代标记，经多数据集验证其有效性，为标注高效的连续情绪唤醒度量化提供了新途径。
中文摘要 AI 辅助
连续情绪唤醒度量化目前受限于耗时且费力的手动标注工作。本研究探究组水平脑电（EEG）动态神经同步性（DNS）作为一种可规避逐被试手动标注的原则性信号，用于连续唤醒度量化。通过对涵盖142名被试、时长超207小时的4个EEG数据集，采用带滑动窗口计算的相关成分分析（CorrCA），系统评估DNS作为情绪唤醒度动态的组水平标记。研究得出三项关键发现：其一，DNS呈现出显著的效价依赖差异的情绪信息（所有p值均小于0.003），积极情绪会引发更高的同步性；其二，DNS与唤醒度的一阶导数的相关性强于与原始唤醒度值的相关性，表明神经同步性捕捉的是情绪变化的速率而非静态强度；其三，本研究首次系统表征了DNS-唤醒度耦合如何依赖关键方法学选择，发现中等窗口（10-30秒）、正滞后（0-10步）以及来自主导CorrCA成分的EEG一阶差分特征，可产生持续强耦合。被试拆分重复与块置换测试证实这些关联并非统计假象。研究结果确立了DNS为经实证验证的组水平标记，助力实现标注高效的连续情绪唤醒度量化。
英文摘要
Continuous emotional arousal quantification remains bottlenecked by time-consuming and labor-intensive manual annotation. This work investigates group-level EEG dynamic neural synchrony (DNS) as a principled signal for continuous arousal quantification that bypasses per-subject manual labeling. Using Correlated Component Analysis (CorrCA) with sliding-window computation across four EEG datasets spanning 142 subjects and over 207 hours, we systematically evaluate DNS as a group-level marker for emotional arousal dynamics. Three key findings emerge. First, DNS exhibits significant emotion information from valence-dependent differences (all p<0.003), with positive emotions eliciting higher synchrony. Second, DNS correlates more strongly with the first-order derivative of arousal than with raw arousal values, revealing that neural synchrony captures the rate of emotional change rather than static intensity. Third, we provide the first systematic characterization of how DNS-arousal coupling depends on key methodological choices, finding that moderate windows (10-30 s), positive lags (0-10 steps), and First-order Difference feature of EEG from the dominant CorrCA component yield consistently strong coupling. Subject-split replication and block permutation tests confirm these associations are not statistical artifacts. Our findings establish DNS as an empirically validated group-level marker toward annotation-efficient continuous emotional arousal quantification.
