Penelope A. Lewis

This study investigates memory reactivation during sleep using targeted memory reactivation, finding faster reactivation and an inverted-U relationship between encoding strength and memory improvement.

The paper detects emotional memory reactivation during sleep using EEG and machine learning, identifying shared neural signatures across participants without individual fine-tuning.

The paper uses computational models of complementary learning systems to study memory replay's role in targeted memory reactivation and birdsong development, showing replay drives performance changes during sleep.

This study shows that targeted memory reactivation during REM sleep reduces emotional responses by decreasing brain activity and physiological arousal to emotional stimuli.

This study uses targeted memory reactivation to detect memory reactivation in human REM sleep, finding it mediated by high theta activity and linked to improved overnight performance.

This study investigates the characteristics of cued memory reactivation during sleep and identifies optimal cue timing for enhancing memory through targeted reactivation.

Wearing an eye mask during sleep improves episodic memory and alertness the next day, with benefits linked to increased slow wave sleep.

This study tracks microstructural changes following memory reactivation during sleep and links them to the emergence of behavioural benefits over time.

This study shows that targeted memory reactivation during slow wave sleep enhances inferential thinking in a transitive inference task, particularly when applied during the Up-phase of the slow oscillation.

This study uses classifiers to predict the effectiveness of closed-loop auditory stimulation during slow-wave sleep based on ongoing neural oscillations.

This study shows that reactivating motor memory during sleep increases precuneus activation and predicts long-term benefits, while also causing structural and functional changes in sensorimotor cortex.

This study examines how auditory closed-loop stimulation affects sleep slow oscillations and memory in older adults compared to younger adults, finding no memory benefit but reduced stimulation effects in older subjects.

This paper develops a machine learning method to detect memory reactivation during sleep using EEG data and finds it is more effective in slow wave sleep than stage 2.