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Inter-channel Granger Causality for Estimating EEG Phase Connectivity Patterns in Dyslexia

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

Abstract

Methods like Electroencephalography (EEG) and magnetoencephalogram (MEG) record brain oscillations and provide 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). An atypical oscillatory sampling could cause the characteristic phonological difficulties of dyslexia at one or more temporal rates; in this sense, measuring the EEG signal can help to make an early diagnosis of DD. The LEEDUCA study conducted a series of EEG experiments on children listening to amplitude modulated (AM) noise with slow-rhythmic prosodic (0.5–1 Hz) to detect differences in perception of oscillatory sampling that could be associated with dyslexia. The evolution of each EEG channel has been studied in the frequency domain, obtaining the analytical phase using the Hilbert transform. Subsequently, the cause-effect relationships between channels in each subject have been reflected thanks to Granger causality, obtaining matrices that reflect the interaction between the different parts of the brain. Hence, each subject was classified as belonging or not to the control group or the experimental group. For this purpose, two ensemble classification algorithms were compared, showing that both can reach acceptable classifying performance in delta band with an accuracy up to 0.77, recall of 0.91 and AUC of 0.97 using Gradient Boosting classifier.

Keywords

  • EEG
  • Dyslexia
  • Granger causality
  • Hilbert
  • Ada Boost
  • Gradient Boosting

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Acknowledgments

This work was supported by projects PGC2018-098813-B-C32 & RTI2018-098913-B100 (Spanish “Ministerio de Ciencia, Innovación y Universidades”), P18-RT-1624, UMA20-FEDERJA-086, CV20-45250, A-TIC-080-UGR18 and P20 00525 (Consejería de econnomía y conocimiento, Junta de Andalucía) and by European Regional Development Funds (ERDF). M.A. Formoso work was supported by Grant PRE2019-087350 funded by MCIN/AEI/10.13039/501100011033 by “ESF Investing in your future”. Work of J.E. Arco was supported by Ministerio de Universidades, Gobierno de España through grant “Margarita Salas”.

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Correspondence to Ignacio Rodríguez-Rodríguez .

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Rodríguez-Rodríguez, I., Ortiz, A., Formoso, M.A., Gallego-Molina, N.J., Luque, J.L. (2022). Inter-channel Granger Causality for Estimating EEG Phase Connectivity Patterns in Dyslexia. 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_6

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  • DOI: https://doi.org/10.1007/978-3-031-06242-1_6

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