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This study investigates the functional connectivity in the theta (4–8 Hz) and beta (13–30 Hz) EEG spectral bands in individuals with ADHD using Explainable Artificial Intelligence (XAI). We compare connectivity patterns between ADHD and non-ADHD groups using XAI-created functional connectivity graphs across different emotional states. Through the computation of functional connectivity matrices, Independent Component Analysis (ICA), and the creation of emotion distance matrices, our analysis reveals notable differences in EEG connectivity, particularly in the theta and beta bands. These findings enhance our understanding of ADHD’s neurobiological underpinnings and suggest potential biomarkers for future clinical applications.

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EEG Spectral Band Analysis in Mental Health: Insights from Explainable AI in Functional Connectivity

  • 会议论文

  • 发表日期：

    2025年

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  • Jing Chang,
  • Amir Assadi

摘要

This study investigates the functional connectivity in the theta (4–8 Hz) and beta (13–30 Hz) EEG spectral bands in individuals with ADHD using Explainable Artificial Intelligence (XAI). We compare connectivity patterns between ADHD and non-ADHD groups using XAI-created functional connectivity graphs across different emotional states. Through the computation of functional connectivity matrices, Independent Component Analysis (ICA), and the creation of emotion distance matrices, our analysis reveals notable differences in EEG connectivity, particularly in the theta and beta bands. These findings enhance our understanding of ADHD’s neurobiological underpinnings and suggest potential biomarkers for future clinical applications.