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bioRxiv · 10.64898/2026.08.25.747119

CREST: A Cortical Resting-State EEG Spatial Transformer for Chronic Pain Inference

Iravantchi, Y.·Lannon, E.·Mackey, S.
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Abstract

Chronic pain mechanisms are complex, spanning multiple brain regions and networks. We ask whether resting brain activity carries a readout of that state. From a few minutes of resting-state electroencephalography (EEG), we generate a spectrogram to represent how each region of the cortex oscillates across frequency and time and pass it through CREST (Cortical Resting-state EEG Spatial Transformer): a frozen image-recognition network that reads each region as an image--here, a spectrogram--paired with a graph model that weighs the 56 cortical regions together to classify chronic-pain status. Across 125 people (74 with chronic pain, 51 healthy controls), evaluated through a leave-one-subject-out cross-validation, CREST separates the two groups with an area under the receiver operating characteristic curve (AUROC) = 0.782 (permutation p < 0.005). Control experiments implicate each persons individual alpha rhythm. Clinical relevanceA resting-state EEG readout of chronic MSK pain could clarify pathophysiology and inform treatment.

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Iravantchi, Y., Lannon, E., Mackey, S.. 2026-09-01. CREST: A Cortical Resting-State EEG Spatial Transformer for Chronic Pain Inference. https://doi.org/10.64898/2026.08.25.747119

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