Demontis D, et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat Genet. 2019;51(1):63–75.

Article  MathSciNet  Google Scholar 

Edition F. Diagnostic and statistical manual of mental disorders. Am Psychiatric Assoc. 2013;21:591–643.

Google Scholar 

Barkley RA, editor. Attention-deficit hyperactivity disorder: A handbook for diagnosis and treatment. New York: Guilford Publication; 2006. p. 3.

Marshall P, Hoelzle J, Nikolas M. Diagnosing attention-deficit/hyperactivity disorder (ADHD) in young adults: a qualitative review of the utility of assessment measures and recommendations for improving the diagnostic process. Clin Neuropsychol. 2021;35(1):165–98.

Article  Google Scholar 

Adamou M, Fullen T, Jones SL. EEG for diagnosis of adult ADHD: A systematic review with narrative analysis. Front Psychiatry. 2020: p. 871.

Lubar JF. Discourse on the development of EEG diagnostics and biofeedback for attention-deficit/hyperactivity disorders. Biofeedback Self Regul. 1991;16(3):201–25.

Article  Google Scholar 

Kaur S, et al. Event-related potential analysis of ADHD and control adults during a sustained attention task. Clin EEG Neurosci. 2019;50(6):389–403.

Article  Google Scholar 

Lau-Zhu A, et al. No evidence of associations between ADHD and event-related brain potentials from a continuous performance task in a population-based sample of adolescent twins. PLoS ONE. 2019;14(10): e0223460.

Article  Google Scholar 

Lenartowicz A, et al. Alpha modulation during working memory encoding predicts neurocognitive impairment in ADHD. J Child Psychol Psychiatry. 2019;60(8):917–26.

Article  Google Scholar 

Rodríguez-Martínez EI, et al. Frequency coupling of low and high frequencies in the EEG of ADHD children and adolescents in closed and open eyes conditions. Res Dev Disabil. 2020;96: 103520.

Article  Google Scholar 

Ponomarev VA, et al. Group independent component analysis (gICA) and current source density (CSD) in the study of EEG in ADHD adults. Clin Neurophysiol. 2014;125(1):83–97.

Article  Google Scholar 

González JJ, et al. Performance analysis of univariate and multivariate EEG measurements in the diagnosis of ADHD. Clin Neurophysiol. 2013;124(6):1139–50.

Article  Google Scholar 

Shi T, et al. EEG characteristics and visual cognitive function of children with attention deficit hyperactivity disorder (ADHD). Brain Develop. 2012;34(10):806–11.

Article  Google Scholar 

Wiegand I, et al. EEG correlates of visual short-term memory as neuro-cognitive endophenotypes of ADHD. Neuropsychologia. 2016;85:91–9.

Article  Google Scholar 

Chenxi L, et al. Complexity analysis of brain activity in attention-deficit/hyperactivity disorder: a multiscale entropy analysis. Brain Res Bull. 2016;124:12–20.

Article  Google Scholar 

Luo X et al. The neural correlations of spatial attention and working memory deficits in adults with ADHD. NeuroImage: Clinical. 2019: p. 101728.

Alperin BR, et al. The relationship between alpha asymmetry and ADHD depends on negative affect level and parenting practices. J Psychiatr Res. 2019;116:138–46.

Article  Google Scholar 

Amisha PM, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. Journal of family medicine and primary care. 2019;8(7):2328.

Article  Google Scholar 

Chen H, Song Y, Li X. A deep learning framework for identifying children with ADHD using an EEG-based brain network. Neurocomputing. 2019;356:83–96.

Article  Google Scholar 

Moghaddari M, Lighvan MZ, Danishvar S. Diagnose ADHD disorder in children using convolutional neural network based on continuous mental task EEG. Comput Methods Programs Biomed. 2020;197: 105738.

Article  Google Scholar 

Allahverdy A, Nasrabadi AM, Mohammadi MR. Detecting ADHD children using symbolic dynamic of nonlinear features of EEG. In2011 19th Iranian Conference on Electrical Engineering 2011. IEEE.

Ekhlasi A, Nasrabadi AM, Mohammadi M. Classification of the children with ADHD and healthy children based on the directed phase transfer entropy of EEG signals. Frontiers in Biomedical Technologies. 2021;8(2):115–22.

Google Scholar 

Sadatnezhad K, Boostani R, Ghanizadeh A. Classification of BMD and ADHD patients using their EEG signals. Expert Syst Appl. 2011;38(3):1956–63.

Article  Google Scholar 

Chen H, Song Y, Li X. Use of deep learning to detect personalized spatial-frequency abnormalities in EEGs of children with ADHD. J Neural Eng. 2019;16(6): 066046.

Article  Google Scholar 

Dubreuil-Vall L, Ruffini G, Camprodon JA. A deep learning approach with event-related spectral EEG data in attentional deficit hyperactivity disorder. medRxiv. 2019: p. 19005611.

Esas MY, Latifoğlu F. Detection of ADHD from EEG signals using new hybrid decomposition and deep learning techniques. J Neural Eng. 2023.

Maniruzzaman M, et al. Optimal channels and features selection based ADHD detection from EEG signal using statistical and machine learning techniques. IEEE Access. 2023;11:33570–83.

Article  Google Scholar 

Kasim Ö. Identification of attention deficit hyperactivity disorder with deep learning model. Phys Eng Sci Med. 2023: p. 1–10.

Seshadri NG, et al. EEG based classification of children with learning disabilities using shallow and deep neural network. Biomed Signal Process Control. 2023;82: 104553.

Article  Google Scholar 

Chugh N, Aggarwal S, Balyan A. The hybrid deep learning model for identification of attention-deficit/hyperactivity disorder using EEG. Clin EEG Neurosci. 2024;55(1):22–33.

Article  Google Scholar 

Loh HW, et al. Deep neural network technique for automated detection of ADHD and CD using ECG signal. Comput Methods Programs Biomed. 2023;241: 107775.

Article  Google Scholar 

Devi D, Sophia S. GA-CNN: analyzing student’s cognitive skills with EEG data using a hybrid deep learning approach. Biomed Signal Process Control. 2024;90: 105888.

Article  Google Scholar 

Nasrabadi AAM, Samavati M, Mohammadi MR. EEG data for ADHD/Control children. IEEE Dataport, 2020.

Talebi N, Nasrabadi AM. Investigating the discrimination of linear and nonlinear effective connectivity patterns of EEG signals in children with attention-deficit/hyperactivity disorder and typically developing children. Comput Biol Med. 2022;148: 105791.

Article  Google Scholar 

Mohammadi MR, et al. EEG classification of ADHD and normal children using non-linear features and neural network. Biomed Eng Lett. 2016;6(2):66–73.

Article  MathSciNet  Google Scholar 

Hu X, et al. Model complexity of deep learning: a survey. Knowl Inf Syst. 2021;63:2585–619.

Article  Google Scholar 

Malik AS, Amin HU. Designing EEG experiments for studying the brain: Design code and example datasets. Academic Press; 2017.

He K et al. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.

Young S, et al. A meta-analysis of the prevalence of attention deficit hyperactivity disorder in incarcerated populations. Psychol Med. 2015;45(2):247–58.

Article  Google Scholar 

Lichtenstein P, et al. Medication for attention deficit–hyperactivity disorder and criminality. N Engl J Med. 2012;367(21):2006–14.

Article  Google Scholar 

Buhrmester V, Münch D, Arens M. Analysis of explainers of black box deep neural networks for computer vision: a survey. Machine Learning and Knowledge Extraction. 2021;3(4):966–89.

Article  Google Scholar