Neuroinspired Interfaces for Human-Machine Interaction

In the present Thesis a new class of natural human-machine interfaces is proposed and discussed: the class of Neuroinspired Interfaces. Defined as systems that rely for their operation on the physiological ways of human (neural) information transfer and (muscular) action execution, Neuroinspired Interfaces can extract information on user's intentions from the following sources: (1) the place where information naturally originates and for the most stays, by monitoring user's brain activity; (2) the place where information’s low-level fully integrated version is naturally conveyed, by monitoring peripheral neural activity; and (3) the place where information naturally manifests itself in form of actions, by monitoring and detecting muscular activity and limb movement. Obviously, the decision on where to extract the information makes a big difference on which kind of information can be extracted, how invasively it can be accessed, and how easily it can be separated from the multitude of information human beings are constantly producing, elaborating, and conveying into actions. The main goal of the present Thesis is to prove the theoretical potentials of Neuroinspired Interfaces at all the three main levels of prospected human-interface connections. The work follows the same "thoughts to actions" path, from the central nervous system (CNS), passing from the peripheral nervous system (PNS), and arriving at the muscular action execution producing limb movements.