Temporal EigenPAC for dyslexia diagnosis

TL;DR

This paper introduces Temporal EigenPAC, a novel EEG-based method combining PAC features and PCA to analyze brain connectivity patterns for dyslexia diagnosis in children, revealing temporal evolution and discriminative features.

Contribution

It presents a new approach to compute PAC-based connectivity among electrodes and applies PCA to extract eigenPACs, enhancing dyslexia detection from EEG data.

Findings

EigenPAC features in Beta-Gamma bands effectively discriminate dyslexic children.

The method reveals temporal evolution of PAC-based connectivity patterns.

Projection onto eigenPACs improves classification accuracy.

Abstract

Electroencephalography 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. However, 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 PAC features among electrodes to study the functional connectivity. Moreover, this has been applied jointly with Principal Component Analysis 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 \textit{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.