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

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Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications (IWINAC 2022)

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

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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.

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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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Authors and Affiliations

  1. Communications Engineering Department, University of Málaga, 29004, Málaga, Spain

    Nicolás J. Gallego-Molina, Andrés Ortiz & Ignacio Rodríguez-Rodríguez

  2. Department of Signal Theory, Communications and Networking, University of Granada, 18060, Granada, Spain

    Francisco J. Martínez-Murcia

  3. Andalusian Data Science and Computational Intelligence Institute (DaSCI), Granada, Spain

    Nicolás J. Gallego-Molina, Andrés Ortiz, Francisco J. Martínez-Murcia & Ignacio Rodríguez-Rodríguez

Authors
  1. Nicolás J. Gallego-Molina
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  2. Andrés Ortiz
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  3. Francisco J. Martínez-Murcia
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  4. Ignacio Rodríguez-Rodríguez
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Correspondence to Nicolás J. Gallego-Molina .

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Editors and Affiliations

  1. Universidad Politécnica de Cartagena, Cartagena, Spain

    José Manuel Ferrández Vicente

  2. Universidad Nacional de Educación a Distancia, Madrid, Spain

    José Ramón Álvarez-Sánchez

  3. Universidad Nacional de Educación a Distancia, Madrid, Spain

    Félix de la Paz López

  4. Ohio State University, Columbus, OH, USA

    Hojjat Adeli

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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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06241-4

  • Online ISBN: 978-3-031-06242-1

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