Javier Escudero

This paper develops a cross-species deep learning model for sleep staging in humans and rodents, achieving high accuracy without retraining.

This study characterizes a Grin2b heterozygous knockout rat model, revealing seizure and sleep-wake abnormalities, and tests the efficacy of two antiseizure drugs.

The paper uses dynamical statistical indicators of locomotor activity in Drosophila to predict infection-related mortality with high accuracy using a Random Forest model.

This study introduces a single-channel EEG artifact detection method using spectral slope, achieving high accuracy in identifying contaminated epochs in rat recordings.

This study analyzes EEG brain state distribution and connectivity in SYNGAP1 mutant rats, finding altered state dynamics and reduced connectivity during sleep spindles.

This study examines hierarchical complexity in neonatal and adult brain networks, finding greater complexity in term-born neonates compared to preterms, linked to connectivity patterns in intermediate tiers.

This paper assesses the hierarchical complexity of human structural connectomes, finding that middle-tier nodes exhibit the highest complexity and align with known functional brain organization.

This paper uses EEG network analysis to detect cognitive impairment in preschool children with epilepsy, achieving 85% sensitivity and 84% specificity.