Signal processing, feature extraction, and machine learning models for EEG-based computer-aided diagnosis of ADHD: a scoping review (2021–2026)

Scoping review mapping the computational pipeline (preprocessing, feature extraction, machine/deep learning models and validation strategies) used for EEG-based computer-aided diagnosis of ADHD in studies published 2021–2026. Conducted following JBI guidance and reported according to PRISMA-ScR.

Authors

Publication Details

Journal
Open Science Framework
Published
2026-09-21
DOI
https://doi.org/10.17605/osf.io/eqkfb
Primary Topic
Attention Deficit Hyperactivity Disorder
Type
article
Field-Weighted Citation Impact
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article

Signal processing, feature extraction, and machine learning models for EEG-based computer-aided diagnosis of ADHD: a scoping review (2021–2026)

Carlos Alberto Castillo-Daza, Luis Eduardo Rodríguez Cheu, Erika Lorena Rosero Alzate, BRAYAN ANDRES RUIZ CORTES
Open Science Framework
Attention Deficit Hyperactivity Disorder
article

Signal processing, feature extraction, and machine learning models for EEG-based computer-aided diagnosis of ADHD: a scoping review (2021–2026)

Carlos Alberto Castillo-Daza, Luis Eduardo Rodríguez Cheu, Erika Lorena Rosero Alzate, BRAYAN ANDRES RUIZ CORTES
article en

Abstract

Scoping review mapping the computational pipeline (preprocessing, feature extraction, machine/deep learning models and validation strategies) used for EEG-based computer-aided diagnosis of ADHD in studies published 2021–2026. Conducted following JBI guidance and reported according to PRISMA-ScR.

Open Science Framework
Openalex Percentile: Top 10%
Attention Deficit Hyperactivity Disorder
DOIOpen Access PDFOpenAlex
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Signal processing, feature extraction, and machine learning models for EEG-based computer-aided diagnosis of ADHD: a scoping review (2021–2026) — Carlos Alberto Castillo-Daza, Luis Eduardo Rodríguez Cheu, et al. · Open Science Framework (2026) | TGRS Research Map | TGRS