Toward Annotation-Efficient Continuous Emotion Arousal Quantification via Group-Level EEG Dynamic Neural Synchrony

2026-07-30

AI Summary

Quantifying continuous emotional arousal typically relies on costly manual annotations, limiting scalability. This study proposes group-level neural dynamical synchrony (DNS) derived from electroencephalography as a label-free proxy for arousal dynamics. Leveraging a large-scale dataset comprising 142 participants and over 207 hours of recordings across multiple experiments, we systematically evaluate the relationship between DNS and emotional arousal using sliding-window Correlated Component Analysis (CorrCA). We find, for the first time, that DNS correlates more strongly with the first derivative of arousal than with its raw values, indicating that neural synchrony reflects the rate of emotional change rather than instantaneous intensity. Furthermore, DNS exhibits robust dependence on window length, lag steps, and component characteristics, showing significant enhancement during positive valence within 10–30 second windows, 0–10 step forward lags, and dominant CorrCA components (p<0.003), thereby validating its efficacy as a scalable, annotation-free marker of group-level affective dynamics.

Abstract

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.

Problem

• continuous emotion arousal
• annotation efficiency
• EEG
• neural synchrony
• group-level analysis

Innovation

• Dynamic Neural Synchrony
• Correlated Component Analysis
• Continuous Emotion Quantification
• Annotation-Efficient
• EEG