{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T06:04:56Z","timestamp":1791353096468,"version":"4.3.1"},"reference-count":43,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>To uncover neurobiological indicators related to autism spectrum disorders and developmental dyslexia, this article gives a full overview of the most recent advances in machine learning and deep learning methods based on electroencephalography. We look into methodological pipelines that include signal gathering, preprocessing, feature engineering, model selection, and interpretability procedures. We based these pipelines on 15 peer-reviewed research papers published between 2013 and 2025. Most of the research employed the 10\u201320 system for resting-state EEG and followed MATLAB, MNE-Python, or EEGLAB guidelines for preprocessing. The feature sets included spectral power, functional connectivity, task-evoked potentials, and entropy measures. People used many standard ML methods, such as support vector machines and random forests, as well as more advanced models, like deep neural networks and transformer-based architectures. Several studies found that both dyslexic and ASD groups did well at classifying, with accuracy scores between 82% and 99.2%. The new models could be used in therapeutic settings, but there are still problems with how easy they are to understand and how well they apply to a wide range of situations. This is especially true for ASD because its spectrum is so varied.<\/jats:p>","DOI":"10.3390\/diagnostics15182388","type":"journal-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T12:33:32Z","timestamp":1758285212000},"page":"2388","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Electroencephalography-Based Machine Learning for Biomarker Detection in Dyslexia and Autism Spectrum Disorder: A Comparative Review of Models, Features, and Diagnostic Utility"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8382-8417","authenticated-orcid":false,"given":"G\u00fcnet","family":"Ero\u011flu","sequence":"first","affiliation":[{"name":"Computer Engineering Department, Engineering and Nature Faculty, Bah\u00e7e\u015fehir University, Istanbul 34000, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"American Psychiatric Association (2013). Diagnostic and Statistical Manual of Mental Disorders, American Psychiatric Publishing. [5th ed.].","DOI":"10.1176\/appi.books.9780890425596"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/S1474-4422(02)00221-1","article-title":"The genetic basis of dyslexia","volume":"1","author":"Francks","year":"2002","journal-title":"Lancet Neurol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1016\/S2215-0366(25)00099-9","article-title":"Convergent evidence linking neonatal vitamin D status and risk of neurodevelopmental disorders: A Danish case-cohort study","volume":"12","author":"Horsdal","year":"2025","journal-title":"Lancet Psychiatry"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1456","DOI":"10.1126\/science.1202529","article-title":"Synaptic pruning by microglia is necessary for normal brain development","volume":"333","author":"Paolicelli","year":"2011","journal-title":"Science"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1016\/j.neuron.2012.03.026","article-title":"Microglia sculpt postnatal neural circuits in an activity and complement-dependent manner","volume":"74","author":"Schafer","year":"2012","journal-title":"Neuron"},{"key":"ref_6","unstructured":"Shaywitz, S.E., and Shaywitz, J. (2020). Overcoming Dyslexia: Completely Revised and Updated, Hachette."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Richlan, F. (2012). Developmental dyslexia: Dysfunction of a left hemisphere reading network. Front. Hum. Neurosci., 6.","DOI":"10.3389\/fnhum.2012.00120"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.1016\/S1388-2457(01)00543-0","article-title":"Alpha and beta band power changes in normal and dyslexic children","volume":"112","author":"Klimesch","year":"2001","journal-title":"Clin. Neurophysiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.1016\/j.biopsych.2006.12.029","article-title":"Excess of high frequency electroencephalogram oscillations in boys with autism","volume":"62","author":"Orekhova","year":"2007","journal-title":"Biol. Psychiatry"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1038\/nrn3978","article-title":"Immune mediators in the brain and peripheral tissues in autism spectrum disorder","volume":"16","author":"Estes","year":"2015","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3165","DOI":"10.1016\/j.clinph.2016.06.023","article-title":"Graph analysis of EEG resting-state functional networks in dyslexic readers","volume":"127","author":"Bonte","year":"2016","journal-title":"Clin. Neurophysiol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gialloreti, L., Enea, R., Di Micco, V., Di Giovanni, D., and Curatolo, P. (2020). Clustering analysis supports the detection of biological processes related to autism spectrum disorder. Genes, 11.","DOI":"10.3390\/genes11121476"},{"key":"ref_13","first-page":"154","article-title":"Quantifying autism spectrum disorder severity through multi-channel EEG-based entropy analysis","volume":"Volume 129","author":"Ali","year":"2025","journal-title":"Proceedings of the 5th International Conference for Innovation in Biomedical Engineering and Life Sciences (ICIBEL 2024)"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Xu, Y., Yu, Z., Li, Y., Liu, Y., Li, Y., and Wang, Y. (2024). Autism spectrum disorder diagnosis with EEG signals using time series maps of brain functional connectivity and a combined CNN\u2013LSTM model. Comput Methods Programs Biomed., 250.","DOI":"10.1016\/j.cmpb.2024.108196"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Fonseca, F.S., Silva, A.s.d.O., Muniz, M.V.S., de Araujo, F.H.A., and Lima, M.d.S.M. (2025). Supporting ASD diagnosis with EEG, ML and swarm intelligence. AI Sens., 1.","DOI":"10.3390\/aisens1010003"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1038\/s41398-023-02681-2","article-title":"Resting-state EEG power differences in autism spectrum disorder: A systematic review and meta-analysis","volume":"13","author":"Neo","year":"2023","journal-title":"Transl. Psychiatry"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"48331","DOI":"10.1007\/s11042-023-17277-7","article-title":"Attention module-based fused deep CNN for learning disabilities identification using EEG signals","volume":"83","author":"Ahire","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"13557","DOI":"10.1007\/s11042-022-13939-0","article-title":"A comprehensive review of machine learning approaches for dyslexia diagnosis","volume":"82","author":"Ahire","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1142\/S0219635207001404","article-title":"Different brain activation patterns in dyslexic children: Evidence from EEG power and coherence patterns for the double-deficit theory of dyslexia","volume":"6","author":"Arns","year":"2007","journal-title":"J. Integr. Neurosci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bosl, W.J., Tager-Flusberg, H., and Nelson, C.A. (2018). EEG analytics for early detection of autism spectrum disorder: A data-driven approach. Sci. Rep., 8.","DOI":"10.1038\/s41598-018-24318-x"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1111\/jcpp.14123","article-title":"Toward a consensus on dyslexia: Findings from a Delphi study","volume":"66","author":"Carroll","year":"2025","journal-title":"J. Child Psychol. Psychiatry"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1002\/hbm.24400","article-title":"Parsing brain structural heterogeneity in males with autism spectrum disorder reveals distinct clinical subtypes","volume":"40","author":"Chen","year":"2019","journal-title":"Hum. Brain Mapp."},{"key":"ref_23","unstructured":"Frid, A., and Manevitz, L.M. (2018). Features and machine learning for correlating and classifying between brain areas and dyslexia. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Priyasri, G.R., and Devi, M.U. (2025, January 18\u201320). Leveraging AI for dyslexia detection: Exploring multimodal approaches with machine learning and deep learning. Proceedings of the 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL), Bhimdatta, Nepal.","DOI":"10.1109\/ICSADL65848.2025.10933253"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Usman, O.L., and Muniyandi, R.C. (2020). CryptoDL: Predicting dyslexia biomarkers from an encrypted neuroimaging dataset using an energy-efficient residue number system and a deep convolutional neural network. Symmetry, 12.","DOI":"10.3390\/sym12050836"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"36879","DOI":"10.1109\/ACCESS.2021.3062709","article-title":"Advanced machine learning methods for dyslexia biomarker detection: A review of implementation details and challenges","volume":"9","author":"Usman","year":"2021","journal-title":"IEEE Access"},{"key":"ref_27","first-page":"S27","article-title":"EEG signal analysis for dyslexia prediction using deep learning techniques","volume":"14","author":"Patil","year":"2025","journal-title":"J. Neonatal Surg."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Karim, I., Abdul, W., and Kamaruddin, N. (2013, January 26\u201327). Classification of dyslexic and normal children during resting condition using KDE and MLP. Proceedings of the 2013 5th International Conference on Information and Communication Technology for the Muslim World (ICT4M), Rabat, Morocco.","DOI":"10.1109\/ICT4M.2013.6518886"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1109\/TPAMI.2010.125","article-title":"Convolutional neural networks for P300 detection with application to brain\u2013computer interfaces","volume":"33","author":"Cecotti","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1914","DOI":"10.1162\/neco_a_01394","article-title":"Classification of autism spectrum disorder from EEG-based functional brain connectivity analysis","volume":"33","author":"Alotaibi","year":"2021","journal-title":"Neural Comput."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ero\u011flu, G. (2025). Electroencephalography-based neuroinflammation diagnosis and its role in learning disabilities. Diagnostics, 15.","DOI":"10.3390\/diagnostics15060764"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1080\/21622965.2022.2074298","article-title":"k-Means clustering by using the calculated Z-scores from QEEG data of children with dyslexia","volume":"12","author":"Arman","year":"2023","journal-title":"Appl. Neuropsychol. Child"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Formoso, M.A., Ortiz, A., Martinez-Murcia, F.J., Gallego, N., and Luque, J.L. (2021). Detecting phase-synchrony connectivity anomalies in EEG signals. Application to dyslexia diagnosis. Sensors, 21.","DOI":"10.3390\/s21217061"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"108098","DOI":"10.1016\/j.knosys.2021.108098","article-title":"Complex network modeling of EEG band coupling in dyslexia: An exploratory analysis of auditory processing and diagnosis","volume":"240","author":"Ortiz","year":"2022","journal-title":"Knowl. Based Syst."},{"key":"ref_35","first-page":"15","article-title":"Early detection of dyslexia based on EEG with novel predictor extraction and selection","volume":"10","author":"Parmar","year":"2023","journal-title":"Brain Inform."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Rezvani, Z., Zare, M., \u017dari\u0107, G., Bonte, M., Tijms, J., Van der Molen, M.W., and Fraga Gonz\u00e1lez, G. (2019). Machine learning classification of dyslexic children based on EEG local network features. bioRxiv.","DOI":"10.1101\/569996"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Rogala, J., \u017bygierewicz, J., Malinowska, U., Kami\u0144ski, J., and Wr\u00f3bel, A. (2023). Enhancing autism spectrum disorder classification in children through EEG analysis. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-49048-7"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Sait, A.R.W., and Alkhurayyif, Y. (2025). Lightweight hybrid transformer-based dyslexia detection using MRI, EEG, and handwriting images. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-01235-4"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Shilaskar, S., Bhatlawande, S., Deshmukh, S., and Dhande, H. (2023, January 1\u20133). Prediction of autism and dyslexia using machine learning and clinical data balancing. Proceedings of the 2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS), Kochi, India.","DOI":"10.1109\/AICAPS57044.2023.10074161"},{"key":"ref_40","first-page":"403","article-title":"Performance of support vector machine in classifying EEG signal of dyslexic children using RBF kernel","volume":"9","author":"Zainuddin","year":"2018","journal-title":"Indones. J. Electr. Eng. Comput. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Chen, Y., Zhang, J., Pan, J., and Wu, D. (2022). Principal subspace of dynamic functional connectivity for diagnosis of autism spectrum disorder. Appl. Sci., 12.","DOI":"10.3390\/app12189339"},{"key":"ref_42","unstructured":"Lundberg, S.M., and Lee, S.-I. (2017, January 4\u20139). A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","article-title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead","volume":"1","author":"Rudin","year":"2019","journal-title":"Nat. Mach. Intell."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/18\/2388\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:49:03Z","timestamp":1760035743000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/18\/2388"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,19]]},"references-count":43,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["diagnostics15182388"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15182388","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,19]]}}}