Cristina Gil Ávila

This study uses EEG to show that within-person pain encoding is stable across time and cohorts, while between-person differences are less consistent.

This study investigates whether peak alpha frequency predicts sensitivity to brief experimental pain and finds no consistent evidence for such a relationship.

This study investigates the effects of centrally acting analgesics on rsEEG biomarkers of chronic pain and finds no significant medium to large effects.

This study investigates whether the aperiodic component of EEG in the medial prefrontal cortex can differentiate between individuals with chronic pain and healthy controls, finding no significant differences.

This study examines intrinsic brain network function in chronic pain using EEG data from 537 individuals, finding some replicable associations but highlighting the need for collaborative research.

This paper introduces new features for characterizing brain states using MEG data and shows they outperform traditional frequency-based features in classification across participants.

DISCOVER-EEG is an open, automated EEG pipeline that preprocesses, analyzes, and visualizes resting state EEG data to extract brain function features, tested on two large datasets.

This study investigates how sensory and expectation effects on pain are mediated by distinct brain mechanisms through local oscillations and inter-regional connectivity.

This study investigates how sensory information and expectations influence pain processing in the brain using EEG, finding that sensory information shapes evoked responses while expectations affect oscillatory activity before stimuli.

This study investigates whether tACS can modulate pain and autonomic responses in healthy humans but finds no significant effects, suggesting the need for refined protocols.

This study uses microstate analysis of resting-state EEG to find that chronic pain patients show reduced prominence of microstate D, associated with attentional networks.