Individualized brain-body signatures of positive emotions revealed by large-scale multimodal neurophysiological recording

Elastic-net models using 128-channel EEG plus 28 autonomic, ocular, and facial signals classified 15 positive and neutral emotions versus baseline in each of four participants with mean accuracy 87–97%.

• Why it matters: Emotions are hard to measure objectively because single biomarkers may not capture how brain and body jointly instantiate affect within individuals.
• What they did: Four participants watched 168 emotion-evoking videos while cross-validated elastic-net classifiers were trained to distinguish each emotion from baseline using multimodal neurophysiological recordings.
• The result: No single marker predicted any emotion; each emotion reflected a largely person-specific multimodal ensemble, while physiology-derived groupings selectively aligned with self-report and recurred for Interest-Awe-Amusement-Satisfaction and Entrancement-Aesthetic Appreciation.

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