“Can we use a biomarker detection algorithm to measure the effectiveness of 14-channel neurofeedback in dyslexia?”

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dc.authorid0009-0007-8239-8604
dc.authorid0009-0001-4214-8738
dc.contributor.authorEroğlu, Günet
dc.contributor.authorHarb, Raja Abou
dc.date.accessioned2025-10-13T07:49:31Z
dc.date.available2025-10-13T07:49:31Z
dc.date.issued2025-10-01
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, one of children’s most common neurological diversities, primarily manifests as a reduced reading ability. Genetic factors contribute to dyslexia, with contemporary theories attributing it to a delay in left hemispheric lateralization that reduces effective reading and writing skills. To assist dyslexic children, smartphone application, Auto Train Brain, has been developed to enhance reading comprehension and speed. Previously, the efficacy of the mobile application’s training program was assessed using psychometric tests; however, our study employed a biomarker detection software to evaluate the neurofeedback’s impact. Machine learning (ML) techniques have recently gained traction in differentiating between dyslexia and typically developing children (TDC). The dataset of this study consists of 100 sessions of 2-minute resting-state eyes-open 14-channel Quantitative Electroencephalography (QEEG) data from 100 children with dyslexia and 100 TDC. Therefore, the dyslexia biomarker detection software assessed the efficacy of the 14-channel neurofeedback administered via Auto Train Brain. Results showed significant improvement in electrophysiological normalization, increasing from 30% in the first 20 sessions to 61% by the end of the training. A two-proportion Z-test confirmed this improvement was statistically significant (Z = −3.96, p = 0.00007), particularly between the 1–20 and 1–60 session intervals (Z = −2.66, p = 0.0079).en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationEroğlu, G. & Harb, R. A. (2025). “Can we use a biomarker detection algorithm to measure the effectiveness of 14-channel neurofeedback in dyslexia?”. Applied Neuropsychology: Child, 1-14. doi:https://doi.org/10.1080/21622965.2025.2545272en_US
dc.identifier.doi10.1080/21622965.2025.2545272
dc.identifier.endpage14
dc.identifier.issn2162-2965
dc.identifier.issn2162-2973
dc.identifier.pmid41032694
dc.identifier.scopus2-s2.0-105017993427
dc.identifier.scopusqualityQ2
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/11729/6759
dc.identifier.urihttps://doi.org/10.1080/21622965.2025.2545272
dc.identifier.wosWOS:001585090800001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakPubMeden_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScience Citation Index Expanded (SCI-EXPANDED)en_US
dc.institutionauthorHarb, Raja Abouen_US
dc.institutionauthorid0009-0001-4214-8738
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherRoutledgeen_US
dc.relation.ispartofApplied Neuropsychology: Childen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial-neural networken_US
dc.subjectAuto train brainen_US
dc.subjectDevelopmental dyslexia biomarkersen_US
dc.subjectDyslexia detectionen_US
dc.subjectQEEGen_US
dc.subjectOscillationsen_US
dc.subjectDeficiten_US
dc.subjectChilden_US
dc.subjectPoweren_US
dc.subjectEEGen_US
dc.title“Can we use a biomarker detection algorithm to measure the effectiveness of 14-channel neurofeedback in dyslexia?”en_US
dc.typeArticleen_US
dspace.entity.typePublicationen_US

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