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dc.contributor.authorGallego-Molina, Nicolás J.
dc.contributor.authorOrtiz-García, Andrés 
dc.contributor.authorMartínez-Murcia, Francisco Jesús
dc.contributor.authorGiménez-de-la-Peña, Almudena 
dc.contributor.authorFormoso, Marco A.
dc.date.accessioned2022-01-11T13:54:28Z
dc.date.available2022-01-11T13:54:28Z
dc.date.created2022-01-11
dc.date.issued2022-01-05
dc.identifier.citationN.J. Gallego-Molina, A. Ortiz, F.J. Martínez-Murcia et al., Complex network modelling of EEG band coupling in dyslexia: An exploratory analysis of auditory processing and diagnosis, Knowledge-Based Systems (2022), doi: https://doi.org/10.1016/j.knosys.2021.108098.es_ES
dc.identifier.urihttps://hdl.handle.net/10630/23570
dc.description.abstractComplex network analysis has an increasing relevance in the study of neurological disorders, enhancing the knowledge of brain’s structural and functional organization. Network structure and efficiency reveal different brain states along with different ways of processing the informa- tion. This work is structured around the exploratory analysis of the brain processes involved in low-level auditory processing. A complex network analysis was performed on the basis of brain coupling obtained from electroencephalography (EEG) data, while different auditory stim- uli were presented to the subjects. This coupling is inferred from the Phase-Amplitude coupling (PAC) from different EEG electrodes to explore differences between control and dyslexic sub- jects. Coupling data allows the construction of a graph, and then, graph theory is used to study the characteristics of the complex networks throughout time for control and dyslexic subjects. This results in a set of metrics including clustering coefficient, path length and small-worldness. From this, different characteristics linked to the temporal evolution of networks and coupling are pointed out for dyslexics. Our study revealed patterns related to Dyslexia as losing the small- world topology. Finally, these graph-based features are used to classify between control and dyslexic subjects by means of a Support Vector Machine (SVM).es_ES
dc.description.sponsorshipThis work was supported by projects PGC2018-098813-B-C32 (Spanish “Ministerio de Cien- cia, Innovación y Universidades”), UMA20-FEDERJA-086 (Consejería de econnomía y conocimiento, Junta de Andalucía) and by European Regional Development Funds (ERDF). We gratefully ac- knowledge the support of NVIDIA Corporation with the donation of one of the GPUs used for this research. Work by F.J.M.M. was supported by the MICINN “Juan de la Cierva - Incorpo- ración” Fellowship. We also thank the Leeduca research group and Junta de Andalucía for the data supplied and the support. Funding for open access charge: Universidad de Málaga / CBUAes_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectDislexia - diagnóstico - Innovaciones tecnológicases_ES
dc.subject.otherDyslexiaes_ES
dc.subject.otherEEGes_ES
dc.subject.otherPACes_ES
dc.subject.otherComplex networkes_ES
dc.titleComplex network modelling of EEG band coupling in dyslexia: An exploratory analysis of auditory processing and diagnosises_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroE.T.S.I. Telecomunicaciónes_ES
dc.identifier.doi10.1016/j.knosys.2021.108098
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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