Data-Driven Identification of Electrophysiological Subgroups in Chronic Traumatic Brain Injury Using EEG and Behavioral Features

Traumatic brain injury (TBI) is associated with heterogeneous neurophysiological and behavioral outcomes that are not always captured by conventional clinical classifications. This study aimed to characterize electrophysiological patterns in individuals with TBI and examine their alignment with clinical groupings. EEG, event-related potentials (ERPs), spectral features, and behavioral performance were analyzed in 121 participants from a larger clinical cohort. Data were preprocessed using artifact subspace reconstruction (ASR), adaptive mixture independent component analysis (AMICA), and standardized quality control procedures. Spectral power, ERP components, and behavioral metrics were extracted and compared across clinical severity and OSU TBI-ID classifications, and unsupervised k-means clustering was applied to identify data-driven electrophysiological subgroups. Results demonstrated consistent ERP signal quality across participants, with all datasets meeting P300 global field power thresholds. While group-level comparisons revealed distributions of electrophysiological markers across clinical categories, clustering identified two electrophysiological subgroups characterized by differing spectral, connectivity, and ERP profiles that were not significantly associated with OSU TBI-ID classifications. Electrophysiological subgroup assignments were not significantly associated with OSU TBI-ID classifications. These findings suggest that data-driven electrophysiological subgrouping captures variability not reflected in traditional clinical groupings, highlighting the potential utility of electrophysiological measures for characterizing heterogeneity within chronic TBI populations.