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Unraveling Dyslexia-Related Connectivity Patterns in EEG Signals by Holo-Hilbert Spectral Analysis

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13258)

Abstract

Neuronal oscillations provide relevant information that helps to understand the neural mechanisms underlying cognitive processes and neural disorders. EEG and MEG methods record these brain oscillations and offer an invaluable insight into healthy and pathological brain function. These signals are helpful to study and achieve an objective and early diagnosis of neural disorders as Developmental Dyslexia (DD). DD early diagnosis is a challenging task that makes possible the application of individualized intervention tasks to dyslexic children in the early stages. In this work, we use EEG signals to explore the neural basis of DD and progress towards an early differential diagnosis. This is achieved by studying Cross-Frequency Coupling (CFC) mechanisms, such as the Phase-Amplitude Coupling (PAC). We apply a recent approach to infer CFC dynamics, the Holo-Hilbert Spectral Analysis (HHSA). This is a further step to overcome the limitations of current PAC methods. We pursue the HHSA over an EEG dataset from the Leeduca project. Then, Holo-Hilbert spectrums are used to explore the changes and patterns of PAC in DD. Finally, the discriminatory capability of Holo-Hilbert spectrums is validated by machine learning techniques.

Keywords

  • HHSA
  • PAC
  • EEG
  • Machine learning
  • Dyslexia diagnosis

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Acknowledgments

This work was supported by projects PGC2018-098813-B-C32 (Spanish “Ministerio de Ciencia, Innovación y Universidades”), UMA20-FEDERJA-086 and P18-RT-1624 (Consejería de economía y conocimiento, Junta de Andalucía), and by European Regional Development Funds (ERDF) as well as the BioSiP (TIC-251) research group. We gratefully acknowledge 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 IJC2019-038835-I MICINN “Juan de la Cierva - Incorporación” Fellowship. We also thank the Leeduca research group and Junta de Andalucía for the data supplied and support.

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Correspondence to Nicolás J. Gallego-Molina .

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Gallego-Molina, N.J., Ortiz, A., Martínez-Murcia, F.J., Rodríguez-Rodríguez, I. (2022). Unraveling Dyslexia-Related Connectivity Patterns in EEG Signals by Holo-Hilbert Spectral Analysis. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications. IWINAC 2022. Lecture Notes in Computer Science, vol 13258. Springer, Cham. https://doi.org/10.1007/978-3-031-06242-1_5

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  • DOI: https://doi.org/10.1007/978-3-031-06242-1_5

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