Bridging the Gap in Pain Measurement with a Brain-Based Index

(1) Background: Pain assessment still relies primarily on subjective self-report. To address these limitations, we developed Piq, an EEG-based index derived from beta-band brain activity (Piq_β) aimed at providing objective pain identification and quantification. (2) Methods: The study combined cross-sectional and longitudinal designs. Resting-state brain activity was recorded for five minutes, and EEG signals were preprocessed using a dedicated algorithm. Piq_β performance was assessed by identifying an optimal cutoff to discriminate pain from no pain, evaluating its association with VNRS, and estimating agreement using a modified concordance criterion (exact match or ±1 category). A graded scale was also established to classify pain into distinct categories, according to intensity. (3) Results: An optimal cutoff of 10% for Piq_β yielded 97.8% sensitivity and 88.2% specificity. Piq_β correlated with self-reported scores (ρ = 0.60, p < 0.0001) with acceptable agreement (mean bias −1.02), accounting for clinically acceptable discrepancies. Five levels of pain were proposed, and Piq_β demonstrated the ability to track intra-individual fluctuations over time, accounting for clinically acceptable discrepancies. (4) Conclusions: These findings provide strong evidence to support the Piq_β index as a valuable complement to subjective pain ratings.