Neurophysiological Characterization of ADHD in Children Using EEG Signals: A Machine Learning Approach to Executive Function Networks.

Overview

1. Carrera de Gestión del Desarrollo Infantil Familiar Comunitario, Facultad de Ciencias de la Educación e Idiomas, Universidad Estatal Península de Santa Elena, La Libertad 240250, Ecuador
2. Universidad Santo Tomas—Seccional Tunja, Boyacá 150001, Colombia
3. Universidad de Salamanca, 37008 Salamanca, Spain
4. Facultad de Sistemas y Telecomunicaciones, Universidad Estatal Península de Santa Elena, La Libertad 240250, Ecuador

Abstract

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder whose clinical assessment relies mainly on behavioral and neuropsychological evaluation. This study evaluates a subject-wise machine learning framework for distinguishing children with ADHD from healthy controls using multichannel EEG-derived features. The public dataset comprised 121 participants (61 ADHD and 60 controls), with 19-channel EEG recordings sampled at 128 Hz. Signals were segmented into 4-s windows with 50% overlap, and statistical and spectral features were extracted, including mean, standard deviation, and theta-, alpha-, and beta-band power. Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting (GB), and Logistic Regression (LR) were evaluated using strict subject-wise separation. RF achieved the highest Accuracy (0.8099), F1-score (0.8160), Balanced Accuracy (0.8097), and MCC (0.6204), whereas SVM obtained the highest Sensitivity (0.8525) and ROC-AUC (0.8527). An additional subject-specific analysis based on individual alpha frequency (IAF) was performed to account for inter-individual spectral variability; mean IAF values were 8.8320 Hz for ADHD and 8.8833 Hz for controls, and the individualized-band analysis did not improve classification performance. Bootstrap confidence intervals and non-parametric tests indicated comparable performance among RF, SVM, and GB. Frontal and fronto-central channels, particularly Fz, showed the greatest model-derived contribution. Overall, the framework provides a reproducible subject-wise EEG classification approach, although external validation on independent cohorts remains necessary before clinical application.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• kaggle.com/datasets/danizo, at Kaggle; found in “Data Availability Statement”

Data Availability Statement

The database used in this study is publicly available and can be accessed through the following repository: EEG Dataset for ADHD: https://www.kaggle.com/datasets/danizo/eeg-dataset-for-adhd [53], accessed on 1 September 2026.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 9 keywords, 12 MeSH terms, 1 funder, 46 references.

Cite

This paper

Gutiérrez-Jácome, D. B., Campos-Ortuño, R. A., Pardo-Valenzuela, J. E., & Gómez-Morales, Ó. W. (2026). Neurophysiological Characterization of ADHD in Children Using EEG Signals: A Machine Learning Approach to Executive Function Networks. Sensors (Basel, Switzerland), 26(17), 5684. https://doi.org/10.3390/s26175684

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