Electroencephalography signatures associated with developmental dyslexia identified using principal component analysis

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dc.authorid0009-0007-8239-8604
dc.authorid0009-0001-4214-8738
dc.contributor.authorEroğlu, Günet
dc.contributor.authorHarb, Mhd Raja Abou
dc.date.accessioned2025-09-26T07:49:10Z
dc.date.available2025-09-26T07:49:10Z
dc.date.issued2025-08-27
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.abstractBackground/Objectives: Developmental dyslexia is characterised by neuropsychological processing deficits and marked hemispheric functional asymmetries. To uncover latent neurophysiological features linked to reading impairment, we applied dimensionality reduction and clustering techniques to high-density electroencephalographic (EEG) recordings. We further examined the functional relevance of these features to reading performance under standardised test conditions. Methods: EEG data were collected from 200 children (100 with dyslexia and 100 age- and IQ-matched typically developing controls). Principal Component Analysis (PCA) was applied to high-dimensional EEG spectral power datasets to extract latent neurophysiological components. Twelve principal components, collectively accounting for 84.2% of the variance, were retained. K-means clustering was performed on the PCA-derived components to classify participants. Group differences in spectral power were evaluated, and correlations between principal component scores and reading fluency, measured by the TILLS Reading Fluency Subtest, were computed. Results: K-means clustering trained on PCA-derived features achieved a classification accuracy of 89.5% (silhouette coefficient = 0.67). Dyslexic participants exhibited significantly higher right parietal–occipital alpha (P8) power compared to controls (mean = 3.77 ± 0.61 vs. 2.74 ± 0.56; p < 0.001). Within the dyslexic group, PC1 scores were strongly negatively correlated with reading fluency (r = −0.61, p < 0.001), underscoring the functional relevance of EEG-derived components to behavioural reading performance. Conclusions: PCA-derived EEG patterns can distinguish between dyslexic and typically developing children with high accuracy, revealing spectral power differences consistent with atypical hemispheric specialisation. These results suggest that EEG-derived neurophysiological features hold promise for early dyslexia screening. However, before EEG can be firmly established as a reliable molecular biomarker, further multimodal research integrating EEG with immunological, neurochemical, and genetic measures is warranted.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationEroğlu, G. & Harb, M. R. A. (2025). Electroencephalography signatures associated with developmental dyslexia identified using principal component analysis. Diagnostics, 15(17), 1-13. doi:https://doi.org/10.3390/diagnostics15172168en_US
dc.identifier.doi10.3390/diagnostics15172168
dc.identifier.endpage13
dc.identifier.issn2075-4418
dc.identifier.issue17
dc.identifier.pmid40941656
dc.identifier.scopus2-s2.0-105016162316
dc.identifier.scopusqualityQ2
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/11729/6723
dc.identifier.urihttps://doi.org/10.3390/diagnostics15172168
dc.identifier.volume15
dc.identifier.wosWOS:001571525900001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakPubMeden_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScience Citation Index Expanded (SCI-EXPANDED)en_US
dc.institutionauthorHarb, Mhd Raja Abouen_US
dc.institutionauthorid0009-0001-4214-8738
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en_US
dc.relation.ispartofDiagnosticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Öğrencien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDevelopmental dyslexiaen_US
dc.subjectElectroencephalography (EEG)en_US
dc.subjectPrincipal Component Analysis (PCA)en_US
dc.subjectReading fluencyen_US
dc.subjectSpectral power asymmetryen_US
dc.titleElectroencephalography signatures associated with developmental dyslexia identified using principal component analysisen_US
dc.typeArticleen_US
dspace.entity.typePublicationen_US

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