A Granularity-Aware EEG Feature Framework for Psychopathology Dimension Prediction

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[Submitted on 2 Jul 2026]

Title:A Granularity-Aware EEG Feature Framework for Psychopathology Dimension Prediction

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Abstract:Electroencephalography (EEG) offers a noninvasive approach for examining neurophysiological correlates of dimensional psychopathology, yet systematic evidence across EEG paradigms and feature granularities remains limited. Here, we develop a granularity-aware EEG feature pipeline that organizes multi-scale descriptors into global, regional, and channel levels. Using the Healthy Brain Network (HBN) cohort, we evaluate the prediction of four psychopathology dimensions: p-factor, internalizing, externalizing, and attention problems, across four EEG paradigms. Given the heterogeneity of pediatric psychopathology and the moderate reliability of questionnaire-derived scores, this setting represents a challenging feasibility test rather than a clinical screening scenario. Tree-based models and granularity-balanced feature selection showed promising improvements over conventional approaches in selected conditions, although effect sizes remained modest. Visualization of selected markers revealed dimension-specific spatial and spectral patterns that were broadly aligned with existing neurophysiological knowledge. An exploratory cross-dataset sanity check on the independent PEARL cohort suggested that the proposed selection principle remains technically feasible under protocol shifts, without claiming cross-dataset generalizability. Overall, multi-scale EEG features contain weak but detectable signals related to dimensional psychopathology, and granularity-aware selection may serve as a useful feature-reduction strategy for future EEG-based phenotyping studies.

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Machine Learning (cs.LG)

Cite as: arXiv:2607.02670 [cs.LG]

(or arXiv:2607.02670v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.02670

arXiv-issued DOI via DataCite

Submission history

From: Haofan Cheng [view email] [v1] Thu, 2 Jul 2026 18:04:53 UTC (1,101 KB)

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