Probabilistic and explainable modeling of Phase–Phase Cross-Frequency Coupling patterns in EEG. Application to dyslexia diagnosis

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This work explores the intricate neural dynamics associated with dyslexia through the lens of Cross-Frequency Coupling (CFC) analysis applied to electroencephalography (EEG) signals evaluated from 48 seven-year-old Spanish readers from the LEEDUCA research platform. The analysis focuses on CFS (Cross-Frequency phase Synchronization) maps, capturing the interaction between different frequency bands during low-level auditory processing stimuli. Then, making use of Gaussian Mixture Models (GMMs), CFS activations are quantified and classified, offering a compressed representation of EEG activation maps. The study unveils promising results specially at the Theta-Gamma coupling (Area Under the Curve = 0.821), demonstrating the method’s sensitivity to dyslexia-related neural patterns and highlighting potential applications in the early identification of dyslexic individuals.

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Diego Castillo-Barnes, Nicolás J. Gallego-Molina, Marco A. Formoso, Andrés Ortiz, Patrícia Figueiredo, Juan L. Luque, Probabilistic and explainable modeling of Phase–Phase Cross-Frequency Coupling patterns in EEG. Application to dyslexia diagnosis, Biocybernetics and Biomedical Engineering, Volume 44, Issue 4, 2024, Pages 814-823, ISSN 0208-5216, https://doi.org/10.1016/j.bbe.2024.09.003. (https://www.sciencedirect.com/science/article/pii/S0208521624000688)

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