Temporal EigenPAC for dyslexia diagnosis
This work addresses dyslexia diagnosis in 7-year-old children using EEG analysis, but it is incremental as it builds on existing CFC and PCA methods.
The authors tackled the problem of diagnosing dyslexia in children by analyzing EEG signals, developing a method to compute cross-frequency coupling features among electrodes and applying PCA to reveal temporal connectivity patterns, achieving discriminative capability in the Beta-Gamma bands.
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.