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Complex network modelling of EEG band coupling in dyslexia: An exploratory analysis of auditory processing and diagnosis Gallego-Molina, Nicolás J. Ortiz-García, Andrés Martínez-Murcia, Francisco Jesús Giménez-de-la-Peña, Almudena Formoso, Marco A. Dislexia - diagnóstico - Innovaciones tecnológicas Complex 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). 2022-01-11T13:54:28Z 2022-01-11T13:54:28Z 2022-01-11T13:54:28Z 2022-01-05 journal article 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. https://hdl.handle.net/10630/23570 10.1016/j.knosys.2021.108098 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier