Electroencephalogram (EEG) Based Prediction of Attention Deficit Hyperactivity Disorder (ADHD) Using Machine Learning

Aug 5, 2026

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Attention Deficit Hyperactivity Disorder (ADHD) requires timely and accurate diagnosis, yet current clinical workflows are labor-intensive and limited by unclear underlying mechanisms. This study tests whether combining electroencephalogram (EEG) recordings with machine learning improves diagnostic performance. Using 168 participants (107 ADHD, 61 neurotypical), EEG from 19 channels across five frequency bands is classified with XGBoost under Leave-One-Subject-Out validation, supported by data augmentation. Results reach 90.81% accuracy (F1=0.9347) and SHAP-driven interpretability highlights informative middle beta features.

LanguageEnglish

CategoryResearch & Report

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Pages9

Reading Time23 min

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Objective and Rationale

Methods

Results

Conclusion

Introduction

Neuropsychiatric Disease and Treatment downloaded from [https://www.dovepress.com/](https://www.dovepress.com/) For personal use only. Neuropsychiatric Disease and Treatment Open Access Full Text Article ORIGINAL RESEARCH Electroencephalogram (EEG) Based Prediction of Attention Deficit Hyperactivity Disorder (ADHD) Using Machine Learning Jun Won Kim 1 , Bung-Nyun Kim2 , Johanna Inhyang Kim 3 , Chan-Mo Yang4 , Jaehyung Kwon 5 1Department of Psychiatry, Daegu Catholic University School of Medicine, Daegu, Republic of Korea; 2Division of Child and Adolescent Psychiatry, Department of Psychiatry, Seoul National University Hospital, Seoul, Republic of Korea; 3Department of Psychiatry, Hanyang University Medical Center, Seoul, Republic of Korea; 4Department of Psychiatry, Wonkwang University Hospital, Iksan, Republic of Korea; 5Affiliated Research Institute of 4N Inc., Daejeon, Republic of Korea Correspondence: Jun Won Kim, Department of Psychiatry, Daegu Catholic University School of Medicine, 33 Duryugongwon-ro 17-gil, Nam-gu, Daegu, Republic of Korea, Tel +82-53-650-4054, Fax +82-53-623-1694, Email [f_affection@hotmail.com](f_affection@hotmail.com) | Objective: Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental condition with challenges in timely and accurate diagnosis. This study evaluates the effectiveness of combining electroencephalogram (EEG) data with machine learning techniques to enhance ADHD diagnostic accuracy.
Methods: A total of 168 participants, comprising 107 ADHD and 61 neurotypical (NT) individuals, were assessed using the Kiddie Schedule for Affective Disorders and Schizophrenia Present and Lifetime Version Korean Version (K-SADS-PL-K) . EEG data from 19 channels were analyzed across five frequency bands: delta (1–4 hz), theta (4–8 hz), alpha (8–12 hz), beta (12–30 hz), and gamma (30–51 hz) . The Extreme Gradient Boosting (XGBoost) classifier was employed for classification, and Leave-One-Subject-Out (LOSO) cross-validation was used to ensure model robustness.
Results: Data augmentation through 30-second segmentations generated 2434 EEG segments for ADHD and 1060 for NT. The XGBoost model achieved a test accuracy of 90.81% and an F1-score of 0.9347. Feature importance analysis using SHAP (SHapley Additive exPlanations) values identified middle beta frequency features, particularly from the O1 electrode site, as significant contributors to classification.
Conclusion: EEG-based machine learning models, such as the XGBoost classifier, show potential as non-invasive tools for ADHD diagnosis, offering high accuracy and interpretability. The novelty of this approach lies in combining SHAP analysis with data augmentation techniques and LOSO cross-validation, ensuring both explainability and robust generalizability. Future research with larger datasets and diverse populations is recommended to validate findings and explore clinical applications.
Keywords: attention deficit hyperactivity disorder, ADHD, machine learning, quantitative electroencephalography, diagnosis | | --- | | Introduction
Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity, affecting both children and adults globally.1 The prevalence of ADHD in children is estimated to be approximately 5%, while around 4.4% of adults are reported to exhibit persistent ADHD symptoms.2 As of 2020, when adjusted for global population characteristics, the prevalence of ADHD starting in childhood and persisting into adulthood was estimated at 2.58%, translating to approximately 139.84 million individuals. Additionally, the prevalence of adult ADHD symptoms unrelated to childhood onset was reported at 6.76%, corresponding to about 366.33 million individuals worldwide. These figures highlight the significant public health burden posed by ADHD on a global scale.3
In addition to core symptoms such as inattention, hyperactivity,

FAQs

How does the study build the ADHD prediction model?

EEG data from 19 channels are processed across five frequency bands and classified using an XGBoost model, with Leave-One-Subject-Out cross-validation to test robustness.

What performance did the XGBoost classifier achieve?

The model achieved 90.81% test accuracy and an F1-score of 0.9347 after data augmentation via 30-second segmentations.

How is the model’s decision explained or interpreted?

Feature importance is assessed using SHAP values, which identify middle beta frequency features—especially from the O1 electrode site—as major contributors to classification.

Aug 5, 2026

Upload by Miles