ArticleHeliyon2024
A novel approach to identify the brain regions that best classify ADHD by means of EEG and deep learning.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- EEG-Based Supported Diagnosis of ADHD Using Subject-Specific HMMs and Stationary RKHS Embeddings.Sensors (Basel, Switzerland) · 2026Article
- QuantumNeuroXAI: a quantum-inspired deep learning framework with explainability for brain signal analysis and neurological disorder detection.Scientific reports · 2026Article
- Integrating socioeconomic context with multimodal EEG data for improved ADHD risk screening.PloS one · 2026Article
- An explainable machine learning-based approach to predicting treatment response for neurofeedback in ADHD.Scientific reports · 2025Article
- MSRLNet: A Multi-Source Fusion and Feedback Network for EEG Feature Recognition in ADHD.Brain sciences · 2025Article
- Early attention-deficit/hyperactivity disorder (ADHD) with NeuroDCT-ICA and rhinofish optimization (RFO) algorithm based optimized ADHD-AttentionNet.Scientific reports · 2025Article
- An adaptive transformer-based framework for advanced brain activity mapping and intelligent neurotherapeutic decision support.Frontiers in human neuroscience · 2025Article
- Enhanced diagnostics for generalized anxiety disorder: leveraging differential channel and functional connectivity features based on frontal EEG signals.Scientific reports · 2024Article
- A novel approach to identify the brain regions that best classify ADHD by means of EEG and deep learning.Heliyon · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Attention-Deficit Hyperactivity Disorder (ADHD) is one of the most widespread neurodevelopmental disorders diagnosed in childhood. ADHD is diagnosed by following the guidelines of Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). According to DSM-5, ADHD has not yet identified a specific cause, and thus researchers continue to investigate this field. Therefore, the primary objective of this work is to present a study to find the subset of channels or brain regions that best classify ADHD vs Typically Developing children by means of Electroencephalograms (EEG). Methods: To achieve this goal, we present a novel approach to identify the brain regions that best classify ADHD using EEG and Deep Learning (DL). First, we perform a filtering and artefact removal process on the EEG signal. Then we generate different subsets of EEG channels depending on their location on the scalp (hemispheres, lobes, sets of lobes and single channels) and using backward and forward stepwise feature selection methods. Finally, we feed the DL neural network with each set, and compute the Results and conclusions: Based on the obtained results, the Frontal Lobe (FL) (0.8081
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