RT Journal Article
T1 Unraveling brain synchronisation dynamics by explainable neural networks using EEG signals. Application to dyslexia diagnosis
A1 Gallego-Molina, Nicolás J.
A1 Ortiz-García, Andrés
A1 Arco, Juan E.
A1 Martinez-Murcia, Francisco J.
A1 Woo, Wai Lok
K1 Redes neuronales (Informática)
K1 Electroencefalografía
K1 Dislexia
AB The electrical activity of the neural processes involved in cognitive functions is captured in EEG signals, allowing the exploration of the integration and coordination of neuronal oscillations across multiple spatiotemporal scales. We have proposed a novel approach that combines the transformation of EEG signal into image sequences, considering cross-frequency phase synchronisation (CFS) dynamics involved in low-level auditory processing, with the development of a two-stage deep learning model for the detection of developmental dyslexia (DD). This deep learning model exploits spatial and temporal information preserved in the image sequences to find discriminative patterns of phase synchronisation over time achieving a balanced accuracy of up to 83%. This result supports the existence of differential brain synchronisation dynamics between typical and dyslexic seven-year-old readers. Furthermore, we have obtained interpretable representations using a novel feature mask to link the most relevant regions during classification with the cognitive processes attributed to normal reading and those corresponding to compensatory mechanisms found in dyslexia.
PB Springer
YR 2024
FD 2024-07
LK https://hdl.handle.net/10630/31983
UL https://hdl.handle.net/10630/31983
LA eng
NO Gallego-Molina NJ, Ortiz A, Arco JE, Martinez-Murcia FJ, Woo WL. Unraveling Brain Synchronisation Dynamics by Explainable Neural Networks using EEG Signals: Application to Dyslexia Diagnosis. Interdiscip Sci. 2024 Jul 2.
NO Funding for open access publishing: Universidad de Málaga / CBUA.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 1 mar 2026