Trial-Level Time-frequency EEG Desynchronization as a Neural Marker of Pain

• URL: http://arxiv.org/abs/2509.10552v1
• Date: Tue, 09 Sep 2025 09:19:23 GMT
• Title: Trial-Level Time-frequency EEG Desynchronization as a Neural Marker of Pain
• Abstract summary: Pain remains one of the most pressing health challenges, yet its measurement still relies heavily on self-report.<n>EEG provides a promising avenue for identifying reproducible markers of nociceptive processing.<n>We analyzed high-density EEG from 59 healthy participants who underwent electrical stimulation under Pain and No-Pain conditions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pain remains one of the most pressing health challenges, yet its measurement still relies heavily on self-report, limiting monitoring in non-communicative patients and hindering translational research. Neural oscillations recorded with electroencephalography (EEG) provide a promising avenue for identifying reproducible markers of nociceptive processing. Prior studies have reported pain-related event-related desynchronization (ERD) in the alpha and beta bands, but most rely on trial-averaging, obscuring variability that may be critical for perception. We analyzed high-density EEG from 59 healthy participants who underwent electrical stimulation under Pain and No-Pain conditions. Per-trial time-frequency decomposition revealed robust beta-band ERD in frontal-central electrodes that differentiated Pain from No-Pain trials. Generalized linear mixed models demonstrated that ERD scaled with subjective intensity ratings (VAS), and that age and gender moderated this relationship. Reverse models further showed that ERD predicted VAS ratings across participants, underscoring its potential as a nonverbal marker of pain. These findings provide preliminary evidence that trial-level EEG oscillations can serve as reliable indicators of pain and open avenues for individualized, report-free pain monitoring. Future work should validate these results in patient populations and extend analyses to multimodal approaches combining EEG, MRI, and attention-based modulation strategies.

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