Cristiano Cuppini

Neurorobotics and Neurorehabilitation

1. Postural and Balance control

Thesis 1 – Integrated analysis of multimodal signals (EEG and force plate) acquired during postural tasks under different sensory conditions, aimed at characterizing the spectral contribution and dynamics of cortical areas involved in balance control.

Thesis 2 – Application of graph-theoretic and functional connectivity measures to characterize inter-regional cortical interactions underlying postural stabilization across different sensory conditions, from combined EEG and force plate data.

2. Fetal Alcohol Spectrum Disorder

Thesis 1 – Characterization of resting-state functional brain connectivity from clinical EEG recordings in a pediatric population with Fetal Alcohol Spectrum Disorder

3. Network dynamics underlying the Sense of Joint Agency during human-human and human-robot interaction

Overall research objective – Although behavioral performance, event-related potentials (ERPs), and beta-band activity are largely comparable during interactions with human and robot partners, the underlying neural mechanisms may differ. This project aims to determine whether distinct patterns of brain functional connectivity and network organization support similar behavioral outcomes during the Sense of Joint Agency in human–human and human–robot interactions.

Thesis 1 – Source-level EEG connectivity during the Sense of Joint Agency in human–human and human–robot interaction

Research objective: to reconstruct cortical source activity and estimate functional brain connectivity to identify the large-scale neural networks and neural interactions underlying the Sense of Joint Agency during collaboration with either a human or a humanoid robot.

Main activities

• Cortical source reconstruction using an inverse solution approach (e.g., eLORETA);
• Extraction of regional time series from cortical regions of interest;
• Estimation of source-level functional connectivity;
• Statistical comparison of whole-brain activation and connectivity patterns across experimental conditions and frequency bands.

Thesis 2 – Graph theoretical characterization of brain functional networks underlying the Sense of Joint Agency during human–human and human–robot interaction

Research objective: to investigate how the topological organization of large-scale brain functional networks is modulated during the Sense of Joint Agency when interacting with either a human or a humanoid robot.

Main activities

• Construction of brain functional networks from source-level connectivity matrices;
• Computation of graph theoretical measures describing network integration, segregation, and communication;
• Statistical comparison of network properties across experimental conditions and frequency bands.

Acquired skills

Through these projects, the student(s) will gain expertise in advanced EEG data analysis and computational neuroscience:

• EEG preprocessing and source reconstruction;
• Source-level functional connectivity analysis;
• Graph theoretical analysis of brain networks;
• Statistical analysis of high-dimensional neurophysiological data;
• MATLAB programming and the use of state-of-the-art open-source toolboxes (e.g., EEGLAB, LORETA, Brain Connectivity Toolbox);
• Data visualization and scientific communication;
• Interpretation of large-scale brain networks within the framework of network neuroscience.