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Arns et al QEEG and ADHD

The attached article describes an approach to ADHD using QEEG to subtype clients and select from 5 types of protocols.

The effects of QEEG-informed neurofeedback in ADHD: An open-label pilot study

Disclaimer: The content below was generated with the assistance of AI and then reviewed and edited by BrainMaster Technologies, Inc. It is provided for educational and informational purposes only and does not constitute medical advice.

The Effects of QEEG-Informed Neurofeedback in ADHD: Summary #

Overview #

This open-label pilot study explored whether QEEG-informed neurofeedback—customizing training based on individual EEG biomarkers—may support improvement in core ADHD symptoms. Twenty-one patients (children and adults) completed personalized neurofeedback protocols based on predefined EEG phenotypes.

Study Purpose #

The study aimed to:

  • Evaluate symptom changes in inattention (ATT), hyperactivity/impulsivity (HI), and comorbid depressive symptoms.

  • Assess whether personalizing neurofeedback using EEG patterns improves clinical outcomes.

  • Examine pre/post changes in EEG power and ERP components (N200, P300).

Methods #

  • Participants: 21 individuals with ADHD/ADD; 7 children and 14 adults.

  • Assessments: QEEG, ERP, ADHD rating scales, and BDI for those with depressive symptoms.

  • Personalization: Neurofeedback protocols selected based on EEG subtype (frontal theta, alpha, low voltage, excess beta).

  • Training: 20–30-minute sessions, 2–3 times weekly.

Key Findings #

1. Symptom Improvement #

  • Significant improvements were observed in:

    • Inattention (ATT) (effect size 1.78)

    • Hyperactivity/Impulsivity (HI) (effect size 1.22)

    • Comorbid depressive symptoms

  • Overall response rate: 76%.

  • Improvements were similar for children and adults.

2. EEG and ERP Changes #

Among the SMR-trained subgroup:

  • N200 and P300 amplitudes increased, suggesting improved stimulus discrimination and attentional processing.

  • SMR power decreased, indicating frequency-specific training effects rather than general EEG changes.

3. Predictive Biomarker Insight #

  • A slower anterior alpha peak frequency (iAPF) predicted less improvement in depressive symptoms, aligning with prior depression research.

  • iAPF did not predict ADHD symptom response.

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