About me

"... accepimus peritura perituri"
(SENECA; de Providentia; V, 6-8)

PhD Candidate in Biorobotics, I am driven by a profound interest in Neuroscience, Brain-Machine Interfaces (BMI), and AI-driven solutions for rehabilitation.

I have a solid engineering background, complemented by hands-on experience acquired in clinical settings and conducting experiments with technical instruments and biological tissues. With my work, I aim to make significant contributions to the field of biomedical solutions, enhancing the well-being of patients affected by neural disabilities.

Currently involved in:

  • Current project

    PhD in Biorobotics @ Scuola Superiore Sant'Anna

    Bioelectronics and Bioengineering Area
    Developing personalized neuroprostheses to improve the quality of life of disabled people by exploiting the potentials of neuroscience-driven approaches. Working in particular, on the development of novel implantable neural interfaces, neuroprosthetic technologies to restore locomotion and grasping sensory-motor functions, bionic artificial limbs, advanced computational algorithms and novel approaches to understand motor control.

Fascinated by:

  • Neuron

    Neural Coding & Modeling

    Neurosensorial information coding and networks emergent behaviours.

  • Central processing unit

    Electronic Design for Prosthetics

    Function restoration devices such as Retinal prostheses and Cochlear Implants.

  • BCI

    AI & Brain Computer Interfaces

    BCIs for enhancement of human capabilities and rehabilitation of impaired function, leveraging on AI.

  • Coding

    Software Development

    Implementation of optimized signal processing algorithms and machine learning models.

Resume

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Publications

  1. Cortico-Muscular Coupling to Control a Hybrid Brain-Computer Interface for Upper Limb Motor Rehabilitation: A Pseudo-Online Study on Stroke Patients.

    [Link] - Year of publication: 2022

    de Seta, V.; Toppi, J.; Colamarino, E.; Molle, R.; Castellani, F.; Cincotti, F.; Mattia, D.; Pichiorri, F.
    Journal: Frontiers Human Neuroscience 2022, 16, 1016862.

Experience

  1. Institut de La Vision, Paris, FR

    Research Internship : Oct 2023 - Apr 2024

    Visual Information Processing Research Team: Neural Coding and Vision Restoration.
    Tasks:
    - Research biological neural network behaviours through Retinal Ganglion Cells (RGC) response analysis, during natural movies elicitatation.
    - Assist multi-electrode array experiments on ex-vivo mouse retina.
    - Analyse and model the electrophysiological response of retinal ganglion neurons, to uncover visual features extracted at this level of the visual pathway, focusing on recently discovered contrast encoding.

    Tools
    Python, Jupyter Notebook, Linux/Unix System

  2. S.Lucia Foundation IRCCS, Rome, IT

    Research Engineer : Sep 2021 - Sep 2023 RECOMMENCER Project: Currently undergoing clinical trial, more information here.

    Brain Computer Interface (BCI) implementation. Corticomuscular Coherence-based BCI for rehabilitation of the upper limb on post-stroke subjects.
    Tasks:
    - Develop real-time computational core for BCI that performed EEG and EMG signal analysis.
    - Create feature visualization and rehab session management UI.
    - Design and integrate information processing modules in a cohesive data pipeline, from acquisition to sensorial neurofeedback.
    - Write documentation and software version managing.

    Tools:
    Matlab, Python, Jupyter Notebook, XML, OpenVibe

Education

  1. Scuola Superiore Sant'Anna, Pisa, IT

    PhD Biorobotics [since Oct 2024]:

    Thesis: currently under development.

  2. Politecnico di Milano, Milan IT

    MSc Biomedical Engineering - Technologies for Electronics [Sep 2021 - Jul 2024]:

    Thesis: Retinal Ganglion Cells responses to color natural scenes: a method for visual stimulation during Multi Electrode Array recordings.
    Thesis is available at the following link.

  3. Sapienza Università di Roma, Rome, IT

    BSc Clinical Engineering [Sep 2018 - Oct 2021]:

    Thesis: Coherence-Based BCI for Rehabilitation: Feature Extraction and Experimental Assessment.
    Tasks:
    - Perform research on state of-the-art use of coherence-based BCI.
    - Implementing via Python, a feature extraction algorithm executable within the OpenVibe Software framework.
    - Conduct laboratory test of features extraction from non-pathological subjects.

  4. Conservatory of Music Santa Cecilia, Rome, IT

    Jazz Drum [2011-2015] & Electronic Music [2018-2019] :

    Completed coursework in composition, music theory, practical application of signal theory to sound.
    Proficient in solfége, piano and drum techniques, as well as orchestral performance.

Projects

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