Unsupervised Stratification of Chronic Pain Patients Using EEG Peak Alpha Spatial Signatures

Chronic pain is a widely prevalent and difficult to treat disease due to the heterogeneity of the patient experience and lack of objective biomarkers to characterize its physiology. There has been recent interest in a specific brain oscillation (alpha) measurable with electroencephalography (EEG) as potentially linked to the specific phenotype of chronic pain. In this study, we investigate a fully data-driven approach to identifying the exact frequency of this alpha oscillation and characterizing its spatial variation across the scalp in a patient-specific manner. We also cluster patients based on this spatial signature using unsupervised methods and show that this clustering has potential for clinical relevance based on self-reported pain scores.Clinical Relevance: Objective, data-driven stratification of chronic pain patients using EEG enables personalized disease management and physiologic insight into varied disease etiology.