Toward Annotation-Efficient Continuous Emotion Arousal Quantification via Group-Level EEG Dynamic Neural Synchrony
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 e
Record details
Published: 30 July 2026
Source: arXiv cs.HC
Category: Research
Topics: Jobs & economy · Finance, VC & PE
Retrieved: 31 July 2026
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ethics.ai (30 July 2026), “Toward Annotation-Efficient Continuous Emotion Arousal Quantification via Group-Level EEG Dynamic Neural Synchrony,” evidence record 15203, https://ethics.ai/record/15203 (originally published by arXiv cs.HC).
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