ArticleFrontiers in neuroscience2020
Deep Learning Convolutional Neural Networks Discriminate Adult ADHD From Healthy Individuals on the Basis of Event-Related Spectral EEG.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05354232 (Home-based Transcranial Direct Current Stimulation), which is not on this map. Cited by 28 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Home-based Transcranial Direct Current Stimulation (tDCS) for Treatment of Attention-deficit/Hyperactivity Disorder (ADHD)
Who cites it
28 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Executive function and neural oscillations in adults with attention-deficit/hyperactivity disorder: a systematic review.Frontiers in neuroscience · 2025Pooled it
- Individualized prediction models in ADHD: a systematic review and meta-regression.Molecular psychiatry · 2024Pooled it
- Artificial Intelligence, Machine Learning, and Deep Learning Approaches for Attention-Deficit/Hyperactivity Disorder Diagnosis: Protocol for a Scoping Review.JMIR research protocols · 2026Article
- A Multimodal Dataset for Neurophysiological and AI Applications.Scientific data · 2026Article
- Integrating socioeconomic context with multimodal EEG data for improved ADHD risk screening.PloS one · 2026Article
- EEG-Based ADHD Classification Using Autoencoder Feature Extraction and ResNet with Double Augmented Attention Mechanism.Brain sciences · 2025Article
- New avenues for understanding what deep networks learn from EEG.Frontiers in robotics and AI · 2025Article
- ADHD/CD-NET: automated EEG-based characterization of ADHD and CD using explainable deep neural network technique.Cognitive neurodynamics · 2024Article
- Electroencephalogram-Based ConvMixer Architecture for Recognizing Attention Deficit Hyperactivity Disorder in Children.Brain sciences · 2024Article
- Teachers and educators' experiences and perceptions of artificial-powered interventions for autism groups.BMC psychology · 2024Article
- A novel approach to identify the brain regions that best classify ADHD by means of EEG and deep learning.Heliyon · 2024Article
- Data leakage in deep learning studies of translational EEG.Frontiers in neuroscience · 2024Article
- ADHD detection based on human action recognition.Neuroscience applied · 2024Article
- A short report on ADHD detection using convolutional neural networks.Frontiers in psychiatry · 2024Article
- Leveraging Unlabeled Electroencephalographic Data to Predict Neurological Recovery for Comatose Patients Following Cardiac Arrest.Computing in cardiology · 2023Article
- Identification of attention deficit hyperactivity disorder with deep learning model.Physical and engineering sciences in medicine · 2023Article
- LSGP-USFNet: Automated Attention Deficit Hyperactivity Disorder Detection Using Locations of Sophie Germain's Primes on Ulam's Spiral-Based Features with Electroencephalogram Signals.Sensors (Basel, Switzerland) · 2023Article
- Alterations in the Glycan Composition of Serum Glycoproteins in Attention-Deficit Hyperactivity Disorder.International journal of molecular sciences · 2023Article
- Deep-Learning-Based ADHD Classification Using Children's Skeleton Data Acquired through the ADHD Screening Game.Sensors (Basel, Switzerland) · 2022Article
- Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials.Cognitive neurodynamics · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Attention deficit hyperactivity disorder (ADHD) is a heterogeneous neurodevelopmental disorder that affects 5% of the pediatric and adult population worldwide. The diagnosis remains essentially clinical, based on history and exam, with no available biomarkers. In this paper, we describe a convolutional neural network (CNN) with a four-layer architecture combining filtering and pooling, which we train using stacked multi-channel EEG time-frequency decompositions (spectrograms) of electroencephalography data (EEG), particularly of event-related potentials (ERP) from ADHD patients (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.