RT Journal Article
T1 Complex network modelling of EEG band coupling in dyslexia: An exploratory analysis of auditory processing and diagnosis
A1 Gallego-Molina, Nicolás J.
A1 Ortiz-García, Andrés
A1 Martínez-Murcia, Francisco Jesús
A1 Giménez-de-la-Peña, Almudena
A1 Formoso, Marco A.
K1 Dislexia - diagnóstico - Innovaciones tecnológicas
AB Complex network analysis has an increasing relevance in the study of neurological disorders,enhancing the knowledge of brain’s structural and functional organization. Network structureand 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 involvedin low-level auditory processing. A complex network analysis was performed on the basis ofbrain 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 studythe 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 arepointed 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 anddyslexic subjects by means of a Support Vector Machine (SVM).
PB Elsevier
YR 2022
FD 2022-01-05
LK https://hdl.handle.net/10630/23570
UL https://hdl.handle.net/10630/23570
LA eng
NO N.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.
NO This 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 forthis 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 thedata supplied and the support. Funding for open access charge: Universidad de Málaga / CBUA
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 24 ene 2026