RT Conference Proceedings
T1 Inter-channel Granger Causality for Estimating EEG Phase Connectivity Patterns in Dyslexia
A1 Rodríguez-Rodríguez, Ignacio
A1 Ortiz-García, Andrés
A1 Formoso, Marco A.
A1 Gallego-Molina, Nicolás J.
A1 Luque-Vilaseca, Juan Luis
K1 Dislexia
K1 Electroencefalografía
AB Methods like Electroencephalography (EEG) and magnetoencephalogram (MEG) record brain oscillations and provide an invaluable insight into healthy and pathological brain function. These signals are helpful to study and achieve an objective and early diagnosis of neural disorders as Developmental Dyslexia (DD). An atypical oscillatory sampling could cause the characteristic phonological difficulties of dyslexia at one or more temporal rates; in this sense, measuring the EEG signal can help to make an early diagnosis of DD. The LEEDUCA study conducted a series of EEG experiments on children listening to amplitude modulated (AM) noise with slow-rhythmic prosodic (0.5–1 Hz) to detect differences in perception of oscillatory  ampling that could be associated with dyslexia. The evolution of each EEG channel has been studied in the frequency domain, obtaining the analytical phase using the Hilbert transform. Subsequently, the cause-effect relationships between channels in  ach subject have been reflected thanks to Granger causality, obtaining matrices that reflect the interaction between the different parts of the brain. Hence, each subject was classified as belonging or not to the control group or the experimental group. For this purpose, two ensemble classification algorithms were compared, showing that both can reach acceptable classifying  erformance in delta band with an accuracy up to 0.77, recall of 0.91 and AUC of 0.97 using Gradient Boosting classifier.
YR 2022
FD 2022
LK https://hdl.handle.net/10630/24714
UL https://hdl.handle.net/10630/24714
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026