Riccardo Barbieri

This study uses statistical models to analyze neuronal responses to electrical cortical stimulation in mice, achieving high accuracy in stimulus decoding and brain region classification.

This study shows how electrical stimulation affects spiking responses in human cortical organoids, finding parameter-dependent excitatory and inhibitory responses with local circuit contributions.

This paper develops an unsupervised machine learning framework to remove surgical artifacts from EDA data in clinical settings, achieving high sensitivity and specificity.

This study assesses the relationship between behavioral and autonomic dynamics during propofol-induced unconsciousness using statistical models and physiological data from nine volunteers.

The paper models EDA pulse amplitudes as differences of inverse Gaussian distributions, showing they outperform other models in describing physiological sweat production.

The paper analyzes electrodermal activity (EDA) using probability models to characterize its temporal structure and validate them with experimental data.