{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T18:15:41Z","timestamp":1788286541114,"version":"build-2803163510"},"reference-count":67,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Attention-deficit\/hyperactivity disorder (ADHD) affects millions globally, yet current diagnostic approaches rely on subjective behavioral assessments without objective neurophysiological markers. While machine learning on electroencephalogram (EEG) data shows promise for automated ADHD risk screening, current methods focus only on brain signals and ignore socioeconomic factors that strongly affect neurodevelopment and ADHD risk. We introduce a novel multimodal deep learning architecture integrating three complementary streams: temporal EEG dynamics via one-dimensional convolutional-recurrent networks, spectro-temporal patterns via two-dimensional convolutional networks with spatial and channel attention, and socioeconomic context via feedforward processing, combined through an attention-based fusion mechanism. Using the Cognitive Electrophysiology in Socioeconomic Context dataset, we evaluate performance across four cognitive tasks with 5-fold stratified cross-validation, ablation studies and benchmarking against a state-of-the-art EEG classification model. Under epoch-level cross-validation, the multimodal approach outperforms EEG-only baselines across all four tasks, achieving accuracy improvements of 2.1\u20135.9% and sensitivity gains up to 12.2%, with strong positive-class F1-scores (96.4\u201399.8%). Results showed higher epoch-level performance when socioeconomic context was incorporated alongside neurophysiological signals, a pattern that held across diverse cognitive paradigms. Leave-One-Subject-Out Cross-Validation across all four tasks yielded accuracy of 0.86\u20130.91 for the EEG-only model and 0.92\u20130.96 for the multimodal model, with sensitivity of 0.71\u20130.96 and specificity of 0.95\u20131.00 for the multimodal model. These subject-independent estimates are more modest than the epoch-level figures and McNemar\u2019s test on paired predictions did not reach significance on any task. The EEG backbone, evaluated without modification on an independent paediatric dataset, also achieved 80.4% subject-independent accuracy, outperforming the prior benchmark. Labels derive from a validated self-report screening instrument rather than clinical diagnosis; this model should be understood as a proof-of-concept for ADHD risk screening, not a diagnostic tool. This work suggests the feasibility of context-aware ADHD risk screening that accounts for environmental influences on neurodevelopment alongside neurophysiological signals.<\/jats:p>","DOI":"10.1371\/journal.pone.0357213","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T17:39:11Z","timestamp":1788284351000},"page":"e0357213","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrating socioeconomic context with multimodal EEG data for improved ADHD risk screening"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4801-9747","authenticated-orcid":true,"given":"Sadikul Haque","family":"Sadi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Arman","family":"Hossain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6100-4895","authenticated-orcid":true,"given":"Md. Nurul Ahad","family":"Tawhid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,9,1]]},"reference":[{"issue":"2","key":"pone.0357213.ref001","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1093\/ije\/dyt261","article-title":"ADHD prevalence estimates across three decades: an updated systematic review and meta-regression analysis","volume":"43","author":"GV Polanczyk","year":"2014","journal-title":"Int J Epidemiol"},{"issue":"1","key":"pone.0357213.ref002","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/s13034-022-00462-1","article-title":"Current state of knowledge on the prevalence of neurodevelopmental disorders in childhood according to the DSM-5: a systematic review in accordance with the PRISMA criteria","volume":"16","author":"L Franc\u00e9s","year":"2022","journal-title":"Child Adolesc Psychiatry Ment Health"},{"issue":"3","key":"pone.0357213.ref003","doi-asserted-by":"crossref","first-page":"220","DOI":"10.4103\/0019-5545.117131","article-title":"Diagnostic and statistical manual of mental disorders 5: a quick glance","volume":"55","author":"VN Vahia","year":"2013","journal-title":"Indian J Psychiatry"},{"issue":"1","key":"pone.0357213.ref004","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s11136-010-9722-5","article-title":"Health-related quality of life of Iranian children with attention deficit\/hyperactivity disorder","volume":"20","author":"P Jafari","year":"2011","journal-title":"Qual Life Res"},{"issue":"1","key":"pone.0357213.ref005","first-page":"1","article-title":"Prevalence and correlates of psychiatric disorders in a National Survey of Iranian Children and Adolescents","volume":"14","author":"MR Mohammadi","year":"2019","journal-title":"Iran J Psychiatry"},{"issue":"6","key":"pone.0357213.ref006","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1016\/j.clinph.2011.10.006","article-title":"Clinically-oriented monitoring of acute effects of methylphenidate on cerebral hemodynamics in ADHD children using fNIRS","volume":"123","author":"Y Monden","year":"2012","journal-title":"Clin Neurophysiol"},{"key":"pone.0357213.ref007","doi-asserted-by":"crossref","unstructured":"Matza L, Paramore C, Prasad M. A review of the economic burden of ADHD: cost effectiveness and resource allocation. Cost Eff Resour Alloc. 2005. doi: 10.1186\/1478-7547-3-5","DOI":"10.1186\/1478-7547-3-5"},{"key":"pone.0357213.ref008","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1016\/j.jad.2023.07.071","article-title":"The global prevalence of attention deficit hyperactivity disorder in children and adolescents: an umbrella review of meta-analyses","volume":"339","author":"G Ayano","year":"2023","journal-title":"J Affect Disord"},{"key":"pone.0357213.ref009","doi-asserted-by":"crossref","first-page":"271","DOI":"10.2147\/NDT.S509094","article-title":"Electroencephalogram (EEG) based prediction of attention deficit hyperactivity disorder (ADHD) using machine learning","volume":"21","author":"JW Kim","year":"2025","journal-title":"Neuropsychiatr Dis Treat"},{"issue":"3","key":"pone.0357213.ref010","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1080\/15622975.2019.1605198","article-title":"EEG\/ERP-based biomarker\/neuroalgorithms in adults with ADHD: development, reliability, and application in clinical practice","volume":"21","author":"A M\u00fcller","year":"2020","journal-title":"World J Biol Psychiatry"},{"issue":"5","key":"pone.0357213.ref011","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1177\/1087054712460087","article-title":"A decade of EEG Theta\/Beta Ratio Research in ADHD: a meta-analysis","volume":"17","author":"M Arns","year":"2013","journal-title":"J Atten Disord"},{"issue":"11","key":"pone.0357213.ref012","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1007\/s11920-014-0498-0","article-title":"Use of EEG to diagnose ADHD","volume":"16","author":"A Lenartowicz","year":"2014","journal-title":"Curr Psychiatry Rep"},{"issue":"1","key":"pone.0357213.ref013","doi-asserted-by":"crossref","first-page":"95","DOI":"10.3390\/brainsci15010095","article-title":"EEG-based ADHD classification using autoencoder feature extraction and ResNet with double augmented attention mechanism","volume":"15","author":"J Bansal","year":"2025","journal-title":"Brain Sci"},{"issue":"3","key":"pone.0357213.ref014","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1016\/j.bbe.2024.07.003","article-title":"Detection of attention deficit hyperactivity disorder based on EEG feature maps and deep learning","volume":"44","author":"O Karabiber Cura","year":"2024","journal-title":"Biocybern Biomed Eng"},{"issue":"4","key":"pone.0357213.ref015","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1111\/j.1467-7687.2012.01147.x","article-title":"Neural correlates of socioeconomic status in the developing human brain","volume":"15","author":"KG Noble","year":"2012","journal-title":"Dev Sci"},{"issue":"9","key":"pone.0357213.ref016","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1038\/nrn2897","article-title":"Socioeconomic status and the brain: mechanistic insights from human and animal research","volume":"11","author":"DA Hackman","year":"2010","journal-title":"Nat Rev Neurosci"},{"issue":"6","key":"pone.0357213.ref017","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0128248","article-title":"Socioeconomic associations with ADHD: findings from a mediation analysis","volume":"10","author":"AE Russell","year":"2015","journal-title":"PLoS One"},{"issue":"1","key":"pone.0357213.ref018","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1038\/s41597-025-05209-z","article-title":"Cognitive electrophysiology in socioeconomic context in adulthood","volume":"12","author":"E Isbell","year":"2025","journal-title":"Sci Data"},{"key":"pone.0357213.ref019","doi-asserted-by":"crossref","first-page":"251","DOI":"10.3389\/fnins.2020.00251","article-title":"Deep learning convolutional neural networks discriminate adult ADHD from healthy individuals on the basis of event-related spectral EEG","volume":"14","author":"L Dubreuil-Vall","year":"2020","journal-title":"Front Neurosci"},{"issue":"5","key":"pone.0357213.ref020","doi-asserted-by":"crossref","first-page":"051001","DOI":"10.1088\/1741-2552\/ab260c","article-title":"Deep learning-based electroencephalography analysis: a systematic review","volume":"16","author":"Y Roy","year":"2019","journal-title":"J Neural Eng"},{"key":"pone.0357213.ref021","doi-asserted-by":"crossref","first-page":"103","DOI":"10.3389\/fncom.2017.00103","article-title":"Classification of EEG signals based on pattern recognition approach","volume":"11","author":"HU Amin","year":"2017","journal-title":"Front Comput Neurosci"},{"issue":"2","key":"pone.0357213.ref022","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1007\/s13534-016-0218-2","article-title":"EEG classification of ADHD and normal children using non-linear features and neural network","volume":"6","author":"MR Mohammadi","year":"2016","journal-title":"Biomed Eng Lett"},{"key":"pone.0357213.ref023","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.sbspro.2012.01.024","article-title":"Using non-linear features of EEG for ADHD\/normal participants\u2019 classification","volume":"32","author":"F Ghassemi","year":"2012","journal-title":"Procedia - Soc Behav Sci"},{"issue":"3","key":"pone.0357213.ref024","doi-asserted-by":"crossref","first-page":"5179","DOI":"10.32604\/cmc.2022.028339","article-title":"Efficient feature selection and machine learning based ADHD detection using EEG signal","volume":"72","author":"Md Maniruzzaman","year":"2022","journal-title":"Comput Mater Continua"},{"issue":"3","key":"pone.0357213.ref025","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1016\/j.bbe.2020.04.006","article-title":"Diagnosis of attention deficit hyperactivity disorder with combined time and frequency features","volume":"40","author":"M Alt\u0131nkaynak","year":"2020","journal-title":"Biocybern Biomed Eng"},{"issue":"6","key":"pone.0357213.ref026","doi-asserted-by":"crossref","first-page":"1350","DOI":"10.1109\/TCDS.2025.3556888","article-title":"Electroencephalogram-based unified approach for multiple neurodevelopmental disorders detection in children using successive multivariate variational mode decomposition","volume":"17","author":"U Chandela","year":"2025","journal-title":"IEEE Trans Cogn Dev Syst"},{"issue":"6","key":"pone.0357213.ref027","article-title":"A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG","volume":"16","author":"MNA Tawhid","year":"2021","journal-title":"PLoS One"},{"key":"pone.0357213.ref028","doi-asserted-by":"crossref","first-page":"105738","DOI":"10.1016\/j.cmpb.2020.105738","article-title":"Diagnose ADHD disorder in children using convolutional neural network based on continuous mental task EEG","volume":"197","author":"M Moghaddari","year":"2020","journal-title":"Comput Methods Programs Biomed"},{"key":"pone.0357213.ref029","doi-asserted-by":"crossref","unstructured":"Cisotto G, Zanga A, Chlebus J, Zoppis I, Manzoni S, Markowska-Kaczmar U. Comparison of attention-based deep learning models for eeg classification. arXiv preprint. 2020. doi: 10.48550\/arXiv.2012.01074","DOI":"10.21203\/rs.3.rs-279263\/v1"},{"issue":"5","key":"pone.0357213.ref030","doi-asserted-by":"crossref","first-page":"1829","DOI":"10.1109\/TCDS.2024.3386364","article-title":"GENet: a generic neural network for detecting various neurological disorders from EEG","volume":"16","author":"MdNA Tawhid","year":"2024","journal-title":"IEEE Trans Cogn Dev Syst"},{"issue":"3","key":"pone.0357213.ref031","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0334389","article-title":"Exploring brain lobe-specific insights in an explainable framework for EEG-based schizophrenia detection","volume":"21","author":"MM Hossain","year":"2026","journal-title":"PLoS One"},{"key":"pone.0357213.ref032","doi-asserted-by":"crossref","first-page":"109305","DOI":"10.1016\/j.compbiomed.2024.109305","article-title":"Refining ADHD diagnosis with EEG: the impact of preprocessing and temporal segmentation on classification accuracy","volume":"183","author":"S Garc\u00eda-Ponsoda","year":"2024","journal-title":"Comput Biol Med"},{"issue":"10","key":"pone.0357213.ref033","doi-asserted-by":"crossref","first-page":"2095","DOI":"10.1111\/ejn.14645","article-title":"EEG spectral power, but not theta\/beta ratio, is a neuromarker for adult ADHD","volume":"51","author":"H Kiiski","year":"2020","journal-title":"Eur J Neurosci"},{"issue":"4","key":"pone.0357213.ref034","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0319487","article-title":"Entropy difference-based EEG channel selection technique for automated detection of ADHD","volume":"20","author":"S Maheshwari","year":"2025","journal-title":"PLoS One"},{"key":"pone.0357213.ref035","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.neubiorev.2021.08.027","article-title":"Socioeconomic status and the developing brain - a systematic review of neuroimaging findings in youth","volume":"130","author":"D Rakesh","year":"2021","journal-title":"Neurosci Biobehav Rev"},{"issue":"3","key":"pone.0357213.ref036","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1080\/09297049.2021.1879766","article-title":"Neural correlates of socioeconomic status in early childhood: a systematic review of the literature","volume":"27","author":"L Olson","year":"2021","journal-title":"Child Neuropsychol"},{"issue":"4","key":"pone.0357213.ref037","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1111\/j.1467-7687.2009.00807.x","article-title":"Differences in the neural mechanisms of selective attention in children from different socioeconomic backgrounds: an event-related brain potential study","volume":"12","author":"C Stevens","year":"2009","journal-title":"Dev Sci"},{"key":"pone.0357213.ref038","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1016\/j.neubiorev.2021.01.022","article-title":"The World Federation of ADHD International Consensus Statement: 208 evidence-based conclusions about the disorder","volume":"128","author":"SV Faraone","year":"2021","journal-title":"Neurosci Biobehav Rev"},{"issue":"2","key":"pone.0357213.ref039","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","article-title":"Multimodal machine learning: a survey and taxonomy","volume":"41","author":"T Baltrusaitis","year":"2019","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"pone.0357213.ref040","doi-asserted-by":"crossref","first-page":"144","DOI":"10.3390\/diagnostics16010144","article-title":"Evaluating the impact of demographic factors on subject-independent EEG-based emotion recognition approaches","volume":"16","author":"N Douglas","year":"2026","journal-title":"Diagnostics (Basel)"},{"key":"pone.0357213.ref041","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1109\/EMBC44109.2020.9175956","article-title":"EEG-based depression detection using convolutional neural network with demographic attention mechanism.","volume-title":"2020 42nd annual international conference of the ieee engineering in medicine & biology society (EMBC)","author":"X Zhang","year":"2020"},{"issue":"1","key":"pone.0357213.ref042","first-page":"1","article-title":"Deep learning for predicting post-stroke cognitive decline using multimodal data: A synthetic proof-of-concept study","volume":"13","author":"R de Filippis","year":"2026","journal-title":"Open Access Library J"},{"issue":"2","key":"pone.0357213.ref043","doi-asserted-by":"crossref","first-page":"1209","DOI":"10.1007\/s10462-021-09986-y","article-title":"Diagnosis and prognosis of mental disorders by means of EEG and deep learning: a systematic mapping study","volume":"55","author":"MJ Rivera","year":"2021","journal-title":"Artif Intell Rev"},{"key":"pone.0357213.ref044","doi-asserted-by":"crossref","first-page":"106676","DOI":"10.1016\/j.compbiomed.2023.106676","article-title":"An explainable and interpretable model for attention deficit hyperactivity disorder in children using EEG signals","volume":"155","author":"SK Khare","year":"2023","journal-title":"Comput Biol Med"},{"key":"pone.0357213.ref045","unstructured":"Motie Nasrabadi A, Allahverdy A, Samavati M, Mohammadi MR. EEG data for ADHD\/Control children. 2020. doi: 10.21227\/rzfh-zn36"},{"issue":"5","key":"pone.0357213.ref046","doi-asserted-by":"crossref","first-page":"987","DOI":"10.3390\/s19050987","article-title":"Removal of artifacts from EEG signals: a review","volume":"19","author":"X Jiang","year":"2019","journal-title":"Sensors (Basel)"},{"issue":"424","key":"pone.0357213.ref047","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1080\/01621459.1993.10476408","article-title":"Alternatives to the median absolute deviation","volume":"88","author":"PJ Rousseeuw","year":"1993","journal-title":"J Am Stat Assoc"},{"issue":"4","key":"pone.0357213.ref048","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1016\/j.jesp.2013.03.013","article-title":"Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median","volume":"49","author":"C Leys","year":"2013","journal-title":"J Exp Soc Psychol"},{"key":"pone.0357213.ref049","doi-asserted-by":"crossref","first-page":"693674","DOI":"10.3389\/fdata.2021.693674","article-title":"A benchmark for data imputation methods","volume":"4","author":"S J\u00e4ger","year":"2021","journal-title":"Front Big Data"},{"issue":"1","key":"pone.0357213.ref050","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1186\/s40537-021-00516-9","article-title":"A survey on missing data in machine learning","volume":"8","author":"T Emmanuel","year":"2021","journal-title":"J Big Data"},{"key":"pone.0357213.ref051","doi-asserted-by":"crossref","first-page":"100080","DOI":"10.1016\/j.cmpbup.2022.100080","article-title":"Detection of ADHD cases using CNN and classical classifiers of raw EEG","volume":"2","author":"B TaghiBeyglou","year":"2022","journal-title":"Comput Methods Programs Biomed Update"},{"key":"pone.0357213.ref052","doi-asserted-by":"crossref","first-page":"33570","DOI":"10.1109\/ACCESS.2023.3264266","article-title":"Optimal channels and features selection based ADHD detection from EEG signal using statistical and machine learning techniques","volume":"11","author":"MD Maniruzzaman","year":"2023","journal-title":"IEEE Access"},{"issue":"5","key":"pone.0357213.ref053","doi-asserted-by":"crossref","first-page":"056013","DOI":"10.1088\/1741-2552\/aace8c","article-title":"EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces","volume":"15","author":"VJ Lawhern","year":"2018","journal-title":"J Neural Eng"},{"key":"pone.0357213.ref054","unstructured":"Loshchilov I, Hutter F. Decoupled weight decay regularization. arXiv preprint. 2017. doi: 10.48550\/arXiv.1711.05101"},{"key":"pone.0357213.ref055","doi-asserted-by":"crossref","unstructured":"Lin TY, Goyal P, Girshick R, He K, Doll\u00e1r P. Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision; 2017. pp. 2980\u20138. doi: 10.1109\/ICCV.2017.324","DOI":"10.1109\/ICCV.2017.324"},{"key":"pone.0357213.ref056","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2022.3217515","article-title":"A Convolutional long short-term memory-based neural network for epilepsy detection from EEG","volume":"71","author":"MDNA Tawhid","year":"2022","journal-title":"IEEE Trans Instrum Meas"},{"issue":"11","key":"pone.0357213.ref057","article-title":"Textural feature based intelligent approach for neurological abnormality detection from brain signal data","volume":"17","author":"MNA Tawhid","year":"2022","journal-title":"PLoS One"},{"issue":"1","key":"pone.0357213.ref058","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1109\/TTS.2023.3239526","article-title":"Automatic and efficient framework for identifying multiple neurological disorders from EEG signals","volume":"4","author":"MDNA Tawhid","year":"2023","journal-title":"IEEE Trans Technol Soc"},{"issue":"2","key":"pone.0357213.ref059","doi-asserted-by":"crossref","DOI":"10.1007\/s12559-025-10447-9","article-title":"Investigating brain lobe biomarkers to enhance dementia detection using EEG data","volume":"17","author":"S Siuly","year":"2025","journal-title":"Cogn Comput"},{"issue":"4","key":"pone.0357213.ref060","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2024.e26028","article-title":"A novel approach to identify the brain regions that best classify ADHD by means of EEG and deep learning","volume":"10","author":"J Sanchis","year":"2024","journal-title":"Heliyon"},{"key":"pone.0357213.ref061","first-page":"28","article-title":"Detecting ADHD children using the attention continuity as nonlinear feature of EEG","volume":"3","author":"A Allahverdy","year":"2016","journal-title":"Front Biomed Technol"},{"key":"pone.0357213.ref062","doi-asserted-by":"crossref","first-page":"105791","DOI":"10.1016\/j.compbiomed.2022.105791","article-title":"Investigating the discrimination of linear and nonlinear effective connectivity patterns of EEG signals in children with attention-deficit\/hyperactivity disorder and typically developing children","volume":"148","author":"N Talebi","year":"2022","journal-title":"Comput Biol Med"},{"issue":"16","key":"pone.0357213.ref063","doi-asserted-by":"crossref","first-page":"7032","DOI":"10.3390\/s23167032","article-title":"LSGP-USFNet: Automated attention deficit hyperactivity disorder detection using locations of Sophie Germain\u2019s primes on ulam\u2019s spiral-based features with electroencephalogram signals","volume":"23","author":"O Atila","year":"2023","journal-title":"Sensors (Basel)"},{"issue":"4","key":"pone.0357213.ref064","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.1007\/s11571-023-10028-2","article-title":"ADHD\/CD-NET: automated EEG-based characterization of ADHD and CD using explainable deep neural network technique","volume":"18","author":"HW Loh","year":"2024","journal-title":"Cogn Neurodyn"},{"issue":"4","key":"pone.0357213.ref065","doi-asserted-by":"crossref","first-page":"966","DOI":"10.1080\/23279095.2023.2247702","article-title":"Electroencephalogram (EEG) based prediction of attention deficit hyperactivity disorder (ADHD) using machine learning","volume":"32","author":"N Ahire","year":"2025","journal-title":"Appl Neuropsychol Adult"},{"issue":"3","key":"pone.0357213.ref066","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1023\/A:1024068626366","article-title":"Inference for the generalization error","volume":"52","author":"C Nadeau","year":"2003","journal-title":"Mach Learn"},{"key":"pone.0357213.ref067","unstructured":"Lundberg SM, Lee SI. A unified approach to interpreting model predictions. Adv Neural Inform Process Syst. 2017;30. doi: 10.48550\/arXiv.1705.07874"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0357213","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T17:39:24Z","timestamp":1788284364000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0357213"}},"subtitle":[],"editor":[{"given":"Phakkharawat","family":"Sittiprapaporn","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,9,1]]},"references-count":67,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9,1]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0357213","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,1]]}}}