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

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 ( C ortical R esting-state E EG S patial T ransformer): 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 person’s individual alpha rhythm.