EEG-Based ADHD Diagnosis in Children Using Channel Attention and Lightweight Deep Learning
By: Zeynep Garip and Ekin Ekinci
References
- American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders, 5th ed., 2013. https://doi.org/10.1176/appi.books.9780890425596
- D. Landínez-Martínez et al., “Neuropsychological and academic performance in Colombian children with attention-deficit hyperactivity disorder: A comparative study with a control group,” Children, vol. 12, no. 5, Apr. 2025, Art. no. 561. https://doi.org/10.3390/children12050561
- G. Polanczyk et al., “The worldwide prevalence of ADHD: a systematic review and metaregression analysis,” American Journal of Psychiatry, vol. 164, no. 6, pp. 942–948, June 2007. https://doi.org/10.1176/ajp.2007.164.6.942
- T. Chen et al., “Diagnosing attention-deficit hyperactivity disorder (ADHD) using artificial intelligence: a clinical study in the UK,” Frontiers in Psychiatry, vol. 14, June 2023, Art. no. 1164433. https://doi.org/10.3389/fpsyt.2023.1164433
- M. Mulraney et al., “Systematic review and meta-analysis: screening tools for attention-deficit/hyperactivity disorder in children and adolescents,” Journal of the American Academy of Child and Adolescent Psychiatry, vol. 61, no. 8, pp. 982–996, Aug. 2021. https://doi.org/10.1016/j.jaac.2021.11.031
- J. Schein et al., “Economic burden of attention-deficit/hyperactivity disorder among children and adolescents in the United States: a societal perspective,” Journal of Medical Economics, vol. 25, no. 1, pp. 193–205, Feb. 2022. https://doi.org/10.1080/13696998.2022.2032097
- F. Atban, E. Ekinci, and Z. Garip, “Traditional machine learning algorithms for breast cancer image classification with optimized deep features,” Biomedical Signal Processing and Control, vol. 81, Mar. 2023, Art. no. 104534. https://doi.org/10.1016/j.bspc.2022.104534
- Ş. Ay, E. Ekinci, and Z. Garip, “A comparative analysis of meta-heuristic optimization algorithms for feature selection on ML-based classification of heart-related diseases,” Journal of Supercomputing, vol. 79, pp. 11797–11826, Mar. 2023. https://doi.org/10.1007/s11227-023-05132-3
- Z. Garip, E. Ekinci, K. Serbest, and S. Eken, “Chaotic marine predator optimization algorithm for feature selection in schizophrenia classification using EEG signals,” Cluster Computing, vol. 27, pp. 11277–11297, May 2024. https://doi.org/10.1007/s10586-024-04511-6
- Z. Wu and C. Guo, “Deep learning and electrocardiography: systematic review of current techniques in cardiovascular disease diagnosis and management,” Biomedical Engineering Online, vol. 24, Feb. 2025, Art. no. 23. https://doi.org/10.1186/s12938-025-01349-w
- E. Sarıgedik et al., “Diagnosis of attention deficit hyperactivity disorder with machine learning methods: A systemic review,” Turkish Journal of Child and Adolescent Mental Health, vol. 31, no. 3, pp. 179–185, 2024. https://doi.org/10.4274/tjcamh.galenos.2022.02486
- N. Ahire, R. N. Awale, and A. Wagh, “Electroencephalogram (EEG) based prediction of attention deficit hyperactivity disorder (ADHD) using machine learning,” Applied Neuropsychology: Adult, vol. 32, no. 4, pp. 966–977, Aug. 2025. https://doi.org/10.1080/23279095.2023.2247702
- J. F. Lubar, “Discourse on the development of EEG diagnostics and biofeedback for attention-deficit/hyperactivity disorders,” Biofeedback and Self-Regulation, vol. 16, no. 3, pp. 201–225, 1991. https://doi.org/10.1007/BF01000016
- E. Gkintoni, A. Vantarakis, and P. Gourzis, “Neuroimaging Insights into the public health burden of neuropsychiatric disorders: A systematic review of electroencephalography-based cognitive biomarkers,” Medicina, vol. 61, no. 6, May 2025, Art. no. 1003. https://doi.org/10.3390/medicina61061003
- N. K. Ahire, “Attention-driven deep learning framework for EEG analysis in ADHD detection,” Applied Neuropsychology: Child, pp. 1–11, May 2025. https://doi.org/10.1080/21622965.2025.2512919
- M. Moghaddari, M. Z. Lighvan, and S. Danishvar, “Diagnose ADHD disorder in children using convolutional neural network based on continuous mental task EEG,” Computer Methods and Programs in Biomedicine, vol. 197, Dec. 2020, Art. no. 105738. https://doi.org/10.1016/j.cmpb.2020.105738
- A. Parashar, N. Kalra, J. Singh, and R. K. Goyal, “Machine learning based framework for classification of children with ADHD and healthy controls,” Intelligent Automation & Soft Computing, vol. 28, no. 3, pp. 669–682, Apr. 2021. https://doi.org/10.32604/iasc.2021.017478
- H. T. Tor et al., “Automated detection of conduct disorder and attention deficit hyperactivity disorder using decomposition and nonlinear techniques with EEG signals,” Computer Methods and Programs in Biomedicine, vol. 200, Mar. 2021, Art. no. 105941. https://doi.org/10.1016/j.cmpb.2021.105941
- Y. Hu et al., “Functional connectivity anomalies in medication-naive children with ADHD: Diagnostic potential, symptoms interpretation, and a mediation model,” Clinical Neurophysiology, vol. 174, pp. 212–219, June 2025. https://doi.org/10.1016/j.clinph.2025.04.011
- A. M. Nasrabadi, A. Allahverdy, M. Samavati, and M. R. Mohammadi, “EEG data for ADHD/Control children,” IEEE DataPort, 2020. [Online]. Available: https://ieee-dataport.org/open-access/eeg-data-adhd-control-children
- U. Hassan and A. Singhal, “Convolutional neural network framework for EEG-based ADHD diagnosis in children,” Health Information Science and Systems, vol. 12, no. 1, Aug. 2024, Art. no. 44. https://doi.org/10.1007/s13755-024-00305-7
- B. Latifi, A. Amini, and A. M. Nasrabadi, “Siamese based deep neural network for ADHD detection using EEG signal,” Computers in Biology and Medicine, vol. 182, Nov. 2024, Art. no. 109092. https://doi.org/10.1016/j.compbiomed.2024.109092
- Y. Sharma and B. K. Singh, “Attention deficit hyperactivity disorder detection in children using multivariate empirical EEG decomposition approaches: A comprehensive analytical study,” Expert Systems with Applications, vol. 213, Mar. 2023, Art. no. 119219. https://doi.org/10.1016/j.eswa.2022.119219
- B. TaghiBeyglou et al., “Detection of ADHD cases using CNN and classical classifiers of raw EEG,” Computer Methods and Programs in Biomedicine Update, vol. 2, 2022, Art. no. 100080. https://doi.org/10.1016/j.cmpbup.2022.100080
- M. Bakhtyari and S. Mirzaei, “ADHD detection using dynamic connectivity patterns of EEG data and ConvLSTM with attention framework,” Biomedical Signal Processing and Control, vol. 76, July 2022, Art. no. 103708. https://doi.org/10.1016/j.bspc.2022.103708
- A. Alim and M. H. Imtiaz, “Automatic identification of children with ADHD from EEG brain waves,” Signals, vol. 4, no. 1, pp. 193–205, Feb. 2023. https://doi.org/10.3390/signals4010010
- M. Z. Ullah, N. Ta, and D. Yu, “A weighted dispersion entropy based EEG analysis for ADHD diagnosis,” Biomedical Signal Processing and Control, vol. 108, Oct. 2025, Art. no. 107993. https://doi.org/10.1016/j.bspc.2025.107993
- M. Y. Esas and F. Latifoğlu, “Detection of ADHD from EEG signals using new hybrid decomposition and deep learning techniques,” Journal of Neural Engineering, vol. 20, no. 3, June 2023, Art. no. 036028. https://doi.org/10.1088/1741-2552/acc902
- T. Cai et al., “Topological feature search method for multichannel EEG: Application in ADHD classification,” Biomedical Signal Processing and Control, vol. 100, Feb. 2025, Art. no. 107153. https://doi.org/10.1016/j.bspc.2024.107153
- H. Jahani and A. A. Safaei, “Efficient deep learning approach for diagnosis of attention-deficit/hyperactivity disorder in children based on EEG signals,” Cognitive Computation, vol. 16, no. 5, pp. 2315–2330, May 2024. https://doi.org/10.1007/s12559-024-10302-3
- Ö. Kasim, “Identification of attention deficit hyperactivity disorder with deep learning model,” Physical and Engineering Sciences in Medicine, vol. 46, pp. 1081–1090, May 2023. https://doi.org/10.1007/s13246-023-01275-y
- S. Aggarwal, N. Chugh, and A. Balyan, “Identification of ADHD disorder in children using EEG based on visual attention task by ensemble deep learning,” in Proc. Int. Conf. Data Sci. Appl.: ICDSA 2022, vol. 2, pp. 243–259, Singapore: Springer Nature Singapore, Feb. 2023. https://doi.org/10.1007/978-981-19-6634-7_18
- V. J. Lawhern et al., “EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces,” Journal of Neural Engineering, vol. 15, no. 5, 2018, Art. no. 056013. https://doi.org/10.1088/1741-2552/aace8c
- M. Zuo, X. Y. Chen, and L. Sui, “A novel STA-EEGNet combined with channel selection for classification of EEG evoked in 2D and 3D virtual reality,” Medical Engineering & Physics, 2025, Art. no. 104363. https://doi.org/10.1016/j.medengphy.2025.104363
- P. P. Mini, T. Thomas, and R. Gopikakumari, “EEG based direct speech BCI system using a fusion of SMRT and MFCC/LPCC features with ANN classifier,” Biomedical Signal Processing and Control, vol. 68, July 2021, Art. no. 102625. https://doi.org/10.1016/j.bspc.2021.102625
- E. U. H. Qazi, A. Almorjan, and T. Zia, “A one-dimensional convolutional neural network (1D-CNN) based deep learning system for network intrusion detection, Applied Sciences, vol. 12, no. 16, Aug. 2022, Art. no. 7986. https://doi.org/10.3390/app12167986
- J. Gan, W. Wang, and K. Lu, “A new perspective: Recognizing online handwritten Chinese characters via 1-dimensional CNN,” Information Sciences, vol. 478, pp. 375–390, Apr. 2019. https://doi.org/10.1016/j.ins.2018.11.035
- M. Wang, J. Yu, H. D. Kim, and A. B. Cruz, “Neural correlates of executive function and attention in children with ADHD: An ALE meta-analysis of task-based functional connectivity studies,” Psychiatry Research, vol. 345, Mar. 2025, Art. no. 116338. https://doi.org/10.1016/j.psychres.2024.116338
- M. Dhoisne et al., “SEEG guided hippocampus-sparing resection in mesial temporal lobe epilepsy,” Neurophysiologie Clinique, vol. 55, no. 3, June 2025, Art. no. 103073. https://doi.org/10.1016/j.neucli.2025.103073
Language: English
Page range: 166 - 174
Submitted on: Apr 15, 2026
Accepted on: Jul 2, 2026
Published on: Jul 20, 2026
Published by: Riga Technical University
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open
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© 2026 Zeynep Garip, Ekin Ekinci, published by Riga Technical University
This work is licensed under the Creative Commons Attribution 4.0 License.