Dr. Vato’s area of study is the development of systems that control artificial devices using brain signals. His research aims to restore lost motor functions for people suffering from stroke, spinal cord injuries, or Parkinson’s disease. These systems, called Brain-computer Interfaces (BCIs), are used to investigate neural mechanisms at the basis of movements and support the rehabilitation process. He obtained his Ph.D. in Bioengineering and Bioelectronics at the University of Genoa (Italy) by investigating neural code. As a postdoctoral fellow at Northwestern University (Chicago), he was involved in an NIH-funded project to develop a bidirectional brain-computer interface. Then, he set up a BCI lab at the Italian Institute of Technology (IIT) in Italy to continue his research activity. He returned to the US to join the National Center for Adaptive Technologies (NCN) in Albany (NY). He is currently developing brain-computer interface systems for rehabilitation based on motor imagery.
Books and Book Chapters
• Semprini M., Boi F. and Vato A. (2016) Bidirectional Brain-Machine Interfaces. In: Closed Loop
Neuroscience, edited by El Hady, Academic Press, San Diego (U.S.), pp. 201-212.
• Vato A. (2015) The cyborgs are coming: neuroscience and bioengineering meet - Arrivano i cyborg.
Dove neuroscienze e bioingengeria si incontrano, Italian language, Hoepli Press, Milan (Italy).
• Bonzano L., Vato A., Chiappalone M. and Martinoia S. (2007) Modulation of electrophysiological
activity in neural networks: towards a bioartificial living system. In: Handbook of Neural Engineering, edited by Matin Akay, Wiley/IEEE Press, New York (U.S.), pp. 29-40.
• Martinoia S., Chiappalone M. and Vato A. (2004) Bioartificial neuronal networks: coupling networks of biological neuron to microtransducer arrays. In: Smart adaptive system on silicon, edited by Valle M., Kluwer Academic Publisher, Boston (U.S.), pp. 285-302.
1. Xie, T., Wu, Z., Schalk, G., Tong, Y., Vato, A., Raviv, N., Guo, Q., Ye, H., Sheng, X., Zhu, X., Brunner,
P. & Chen, L. (2022). Automated intraoperative central sulcus localization and somatotopic mapping using median nerve stimulation. Journal of Neural Engineering, 19(4), 046020.
2. Putzolu, M., Samogin J., Cosentino C., Mezzarobba S., Bonassi G., Lagravinese G., Vato A., Mantini
D., Avanzino L., and Pelosin E. (2022) Neural oscillations during motor imagery of complex gait: an HdEEG study. Scientific Reports 12, 4314.
3. De Feo, V., Boi, F., Safaai, H., Onken, A., Panzeri, S., and Vato, A. (2017) State-dependent decoding
algorithms improve the performance of a bidirectional BMI in anesthetized rats. Frontiers in Neuroscience, 11, 269.
4. Boi F., Moratis T., De Feo V., Diotalevi F., Bartolozzi C., Indiveri G., and Vato A. (2016) A bidirectional
brain-machine interface featuring a neuromorphic hardware decoder. Frontiers in Neuroscience, 10, 563.
5. Panzeri S., Safaai H., De Feo V., and Vato A. (2016) Implications of the dependence of neuronal
activity on neural network states for the design of brain-machine interfaces. Frontiers in Neuroscience, 10, 165.
6. Angotzi G.N., Baranauskas G., Vato A., Bonfanti A., Zambra G., Maggiolini E., Semprini M., Ricci D.,
Ansaldo A, Castagnola E., Ius T., Skrap M., and Fadiga L. (2015) A Compact and Autoclavable System
for Acute Extracellular Neural Recording and Brain Pressure Monitoring for Humans. IEEE Transactions on Biomedical Circuits and Systems 9(1), 50-59.
7. Angotzi G.N., Boi F., Zordan S., Bonfanti A., and Vato A. (2014) A programmable closed-loop recording
and stimulating wireless system for behaving small laboratory animals. Scientific Reports 4, 5963.
8. Vato A., Szymanski F.D., Semprini M., Mussa-Ivaldi F.A., and Panzeri S. (2014) A bidirectional brainmachine interface algorithm that approximates arbitrary force fields. PLoS One 9(3), e91677.