Alessia Cavallo

This study shows that state-dependent deep brain stimulation in Parkinson's disease increases movement speed more effectively during fast movements, using machine learning to decode brain signals for real-time adjustments.

This study identifies how dopamine and deep brain stimulation affect cortex-basal ganglia networks at different scales, showing distinct mesoscale effects and shared macroscale suppression of network synchrony.

This study develops real-time phase-tuned tACS to enhance working memory by adjusting phase lag relative to alpha oscillations, showing improved performance at specific phase lags.

This study uses transcranial stimulation to show that frontal delta oscillations causally affect heartbeat perception, with enhanced synchrony reducing detection accuracy.

This study investigates how dopamine and deep brain stimulation (DBS) affect neural dynamics in Parkinson's disease by measuring motor intention latency and oscillatory communication patterns.

This study uses SASS to directly show that phase-dependent enhancement and suppression of brain oscillations occur during AM-tACS, with behavioral effects predicted by oscillation modulation.

This paper introduces a strategy for brain/neural robot control using fewer scalp electrodes, showing comparable classification accuracy to conventional methods even when participants chew.