Abstract
Electroencephalography (EEG) signals allow to explore the functional activity of the brain cortex in a non-invasive way. However, the analysis of these signals is not straightforward due to the presence of different artifacts and the very low signal-to-noise ratio. Cross-Frequency Coupling (CFC) methods provide a way to extract information from EEG, related to the synchronization among frequency bands. CFC methods are usually applied in a local way, computing the interaction between phase and amplitude at the same electrode. In this work we show a method to compute Phase-Amplitude Coupling (PAC) features among electrodes to study the functional connectivity. Moreover, this has been applied jointly with Principal Component Analysis (PCA) to explore patterns related to Dyslexia in 7-years-old children. The developed methodology reveals the temporal evolution of PAC-based connectivity. Directions of greatest variance computed by PCA are called eigenPACs here, since they resemble the classical eigenfaces representation. The projection of PAC data onto the eigenPACs provide a set of features that has demonstrates their discriminative capability, specifically in the Beta-Gamma bands.
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Acknowledments
This work was supported by project PGC2018-098813-B-C32 (Spanish “Ministerio de Ciencia, Innovaci Universidades”), and by European Regional Development Funds (ERDF). 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 MICINN “Juan de la Cierva - Incorporación” Fellowship. We also thank the Leeduca research group and Junta de Andalucí for the data supplied and the support.
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Gallego-Molina, N.J., Formoso, M., Ortiz, A., Martínez-Murcia, F.J., Luque, J.L. (2021). Temporal EigenPAC for Dyslexia Diagnosis. In: Rojas, I., Joya, G., Català, A. (eds) Advances in Computational Intelligence. IWANN 2021. Lecture Notes in Computer Science(), vol 12862. Springer, Cham. https://doi.org/10.1007/978-3-030-85099-9_4
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