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This paper presents a granularity-aware EEG feature framework that organizes multi-scale descriptors into global, regional, and channel levels to predict dimensional psychopathology. Using the HBN cohort, it shows that tree-based models and granularity-balanced feature selection yield modest improvements, suggesting multi-scale EEG features contain weak but detectable signals for pediatric mental health.