Theta and Beta1 frequency band values predict dyslexia classification

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dc.authorid0009-0007-8239-8604
dc.authorid0009-0001-4214-8738
dc.contributor.authorEroğlu, Güneten_US
dc.contributor.authorHarb, Mhd Raja Abouen_US
dc.date.accessioned2026-01-22T06:58:49Z
dc.date.available2026-01-22T06:58:49Z
dc.date.issued2025-12-29
dc.departmentIşık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans Programıen_US
dc.departmentIşık University, School of Graduate Studies, Master’s Program in Computer Engineeringen_US
dc.description.abstractDyslexia, impacting children's reading skills, prompts families to seek cost-effective neurofeedback therapy solutions. Utilising machine learning, we identified predictive factors for dyslexia classification. Employing advanced techniques, we gathered 14-channel Quantitative Electroencephalography (QEEG) data from 200 participants, achieving 99.6% dyslexic classification accuracy through cross-validation. During validation, 48% of dyslexic children's sessions were consistently classified as normal, with a 95% confidence interval of 47.31 to 48.68. Focusing on individuals consistently diagnosed with dyslexia during therapy, we found that dyslexic individuals exhibited higher theta values and lower beta1 values compared to typically developing children. This study pioneers machine learning in predicting dyslexia classification factors, offering valuable insights for families considering neurofeedback therapy investment.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationEroğlu, G. & Harb, M. R. A. (2025). Theta and Beta1 frequency band values predict dyslexia classification. Dyslexia, 32(1), 1-15. doi:https://doi.org/10.1002/dys.70021en_US
dc.identifier.doi10.1002/dys.70021
dc.identifier.endpage15
dc.identifier.issn1076-9242
dc.identifier.issn1099-0909
dc.identifier.issue1
dc.identifier.pmid41457785
dc.identifier.scopus2-s2.0-105026221369
dc.identifier.scopusqualityQ2
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/11729/6944
dc.identifier.urihttps://doi.org/10.1002/dys.70021
dc.identifier.volume32
dc.identifier.wosWOS:001650062700001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakPubMeden_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakSocial Sciences Citation Index (SSCI)en_US
dc.institutionauthorHarb, Mhd Raja Abouen_US
dc.institutionauthorid0009-0001-4214-8738
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherJohn Wiley and Sons Ltden_US
dc.relation.ispartofDyslexiaen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAuto train brainen_US
dc.subjectDyslexia detectionen_US
dc.subjectQEEGen_US
dc.subjectSupervised machine learning techniquesen_US
dc.subjectAdolescenten_US
dc.subjectBeta rhythmen_US
dc.subjectChilden_US
dc.subjectDyslexiaen_US
dc.subjectElectroencephalographyen_US
dc.subjectMachine learningen_US
dc.subjectNeurofeedbacken_US
dc.subjectTheta rhythmen_US
dc.subjectCognitionen_US
dc.subjectCognitive rehabilitationen_US
dc.subjectConfidence intervalen_US
dc.subjectControlled studyen_US
dc.subjectCost effectiveness analysisen_US
dc.subjectCross validationen_US
dc.subjectDiagnostic accuracyen_US
dc.subjectDisease classificationen_US
dc.subjectNerve cell plasticityen_US
dc.subjectNeurofeedbacken_US
dc.subjectPredictionen_US
dc.subjectQuantitative electroencephalographyen_US
dc.subjectSchool childen_US
dc.subjectSupervised machine learningen_US
dc.subjectClassificationen_US
dc.subjectDiagnosisen_US
dc.subjectPathophysiologyen_US
dc.subjectPhysiologyen_US
dc.subjectCerebral lateralizationen_US
dc.subjectOscillationsen_US
dc.subjectDeficiten_US
dc.subjectPoweren_US
dc.subjectEEGen_US
dc.subjectAssociationsen_US
dc.subjectReaden_US
dc.titleTheta and Beta1 frequency band values predict dyslexia classificationen_US
dc.typeArticleen_US
dspace.entity.typePublicationen_US

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