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.
CITATION STYLE
Gallego-Molina, N. J., Formoso, M., Ortiz, A., Martínez-Murcia, F. J., & Luque, J. L. (2021). Temporal EigenPAC for Dyslexia Diagnosis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12862 LNCS, pp. 45–56). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-85099-9_4
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