Elios Ghinato
Robust classification of a neuromuscular signal for real-time control of a prosthetic hand.
Rel. Valentina Agostini, Marco Ghislieri. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2022
Abstract
New technological processes that allowed the development of ever smaller and more performing electrodes and simultaneously the advancement of research in AI field has made possible to integrate machine with human body, leading to significant progress in neuroscience field. This new technology is expected to allow neuroscientists to make great strides working on brain signals decoding where large number of channels are generally required as in the case of EEG and in particular of ECoG. Eventually, this progress is expected to lead to a new generation of brain-computer Interfaces (BCIs) that have the potential to restore lost sensory motor function, caused for example by spinal cord injury or neurodegenerative disease.
This Master Thesis aims at developing a strategy for Continuous Control (CC) of a prosthetic hand to evaluate how a Machine Learning algorithm can enable a Non-Human-Primate to control a number of DOF
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