ADHDeepNet: An Explainable Framework for EEG-Based ADHD Detection From Raw Signals
4 affiliations
- 1Kaunas University of Technology
- 2University of Central Florida
- 3Amirkabir University of Technology
- 4Shahed University
Abstract
Attention Deficit Hyperactivity Disorder (ADHD) is a common brain disorder in children that can persist into adulthood, affecting social, academic, and career life. Early diagnosis is crucial for managing these impacts on patients and the healthcare system but is often labor-intensive and time-consuming. This paper presents a novel method to improve ADHD diagnosis precision and timeliness by leveraging Deep Learning (DL) approaches and electroencephalogram (EEG) signals. We introduce ADHDeepNet, a deep learning model that utilizes comprehensive temporal-spatial characterization, attention modules, and explainability techniques optimized for EEG signals. ADHDeepNet integrates feature extraction and refinement processes to enhance ADHD diagnosis. The model was trained and validated on a dataset of 121 participants (61 ADHD, 60 Healthy Controls), employing nested cross-validation for robust performance. The proposed two-stage methodology uses a 10-fold cross-subject validation strategy. Initially, each iteration optimizes the model's hyper-parameters with inner 2-fold cross-validation. Then, Additive Gaussian Noise (AGN) with various standard deviations and magnification levels is applied for data augmentation. ADHDeepNet achieved 100% sensitivity and 99.17% accuracy in classifying ADHD/HC subjects. To clarify model explainability and identify key brain regions and frequency bands for ADHD diagnosis, we analyzed the learned weights and activation patterns of the model's primary layers. Additionally, t-distributed Stochastic Neighbor Embedding (t-SNE) visualized high-dimensional data, aiding in interpreting the model's decisions. This study highlights the potential of deep learning and EEG in enhancing ADHD diagnosis accuracy and efficiency.
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BibTeX
@article{amini2026adhdeepnet,
title = {{ADHDeepNet: An Explainable Framework for EEG-Based ADHD Detection From Raw Signals}},
author = {Ali Amini and Mohammad Alijanpour Shalmani and Behnam Latifi and Ali Motie Nasrabadi},
journal = {Biomedical Physics \& Engineering Express},
year = {2026},
month = oct,
doi = {10.1088/2057-1976/aeb67a},
url = {https://doi.org/10.1088/2057-1976/aeb67a},
note = {CC BY},
}
RIS
TY - JOUR
TI - ADHDeepNet: An Explainable Framework for EEG-Based ADHD Detection From Raw Signals
AU - Ali Amini
AU - Mohammad Alijanpour Shalmani
AU - Behnam Latifi
AU - Ali Motie Nasrabadi
PY - 2026
DA - 2026/10/20
JO - Biomedical Physics & Engineering Express
DO - 10.1088/2057-1976/aeb67a
UR - https://doi.org/10.1088/2057-1976/aeb67a
AB - Attention Deficit Hyperactivity Disorder (ADHD) is a common brain disorder in children that can persist into adulthood, affecting social, academic, and career life. Early diagnosis is crucial for managing these impacts on patients and the healthcare system but is often labor-intensive and time-consuming. This paper presents a novel method to improve ADHD diagnosis precision and timeliness by leveraging Deep Learning (DL) approaches and electroencephalogram (EEG) signals. We introduce ADHDeepNet, a deep learning model that utilizes comprehensive temporal-spatial characterization, attention modules, and explainability techniques optimized for EEG signals. ADHDeepNet integrates feature extraction and refinement processes to enhance ADHD diagnosis. The model was trained and validated on a dataset of 121 participants (61 ADHD, 60 Healthy Controls), employing nested cross-validation for robust performance. The proposed two-stage methodology uses a 10-fold cross-subject validation strategy. Initially, each iteration optimizes the model's hyper-parameters with inner 2-fold cross-validation. Then, Additive Gaussian Noise (AGN) with various standard deviations and magnification levels is applied for data augmentation. ADHDeepNet achieved 100% sensitivity and 99.17% accuracy in classifying ADHD/HC subjects. To clarify model explainability and identify key brain regions and frequency bands for ADHD diagnosis, we analyzed the learned weights and activation patterns of the model's primary layers. Additionally, t-distributed Stochastic Neighbor Embedding (t-SNE) visualized high-dimensional data, aiding in interpreting the model's decisions. This study highlights the potential of deep learning and EEG in enhancing ADHD diagnosis accuracy and efficiency.
SN - 2057-1976
ER -