Pavel Prado

This paper introduces a modified neural mass model with inhibitory synaptic plasticity to better simulate EEG and fMRI recordings during NREM sleep and capture the spectral richness of brain activity.

This study identifies disease-specific spectro-temporal signatures in Alzheimer's and Parkinson's disease using EEG and machine learning, revealing increased variability and distinct spectral markers.

This paper uses a physics-based model of brain networks to show that frequency-selective stimulation entrains specific sub-networks, with lower frequencies promoting broader synchrony and higher frequencies producing localized effects, determined by network structure and oscillatory dynamics.

This paper uses deep learning models to analyze whole-brain dynamics in neurodegenerative diseases, identifying key regions for inducing transitions from pathological to healthy states.

This study examines acoustic and electroencephalography changes during the Lombard effect in participants with normal hearing and voice, finding increased vocal intensity and altered ERP responses.

The paper introduces the Chirp Analyzer (CA) for estimating EFR amplitude and latency, showing it outperforms traditional methods in accuracy and robustness to noise.

This study examines how averaging methods affect auditory steady-state response (ASSR) amplitudes in rats, finding that independent epochs yield higher amplitudes and better detection rates.