Silvia Scarpetta

This paper introduces a two-phase deep learning framework for EEG-MEG decoding in BCI, showing improved classification accuracy and transferability across participants.

The paper analyzes neuronal avalanche dynamics in resting-state MEG data to show temporal persistence and structural organization in brain activity.

The paper studies how modular spiking neural networks store and retrieve dynamic spatiotemporal patterns by adjusting temporal overlap between modules.

The paper studies a modular neural network model to show how symmetry breaking leads to different avalanche shapes and critical behavior in brain dynamics.

The paper investigates how structural brain connections enable diverse dynamic patterns by testing if neuronal activity near a critical regime explains structural-functional relationships.

This study shows alpha oscillations in the awake resting-state modulate neural activity through alternating inhibition and excitation bouts, with long-term waxing and waning dynamics not observed during NREM sleep.

The paper characterizes neuronal avalanche criticality during sleep and links it to sleep architecture, showing power law distributions and scaling relations consistent with mean-field directed percolation.

The paper analyzes a 2D stochastic Wilson Cowan model and finds different critical exponents compared to mean field models.