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Neural interfaces and body-machine interfaces

Neural interfaces
In neural interfaces, an artificial device communicates directly with the nervous system with no direct participation of the sensory or the motor systems. Building from research carried out by V. Sanguineti during his post-doc training, the group participated (2002-2005) in a EU-funded project, NEUROBIT, aimed at developing a bidirectional neural interface between an in-vitro neuronal population grown on a multi-electrodes matrix and a mobile robot. The main results (L.Cozzi, P. D’Angelo, R. Alessio) were the experimental characterization, in terms of information theory, of the computational properties of the neuronal population. This research line is the result of a collaboration with the group of S. Martinoia at this University and other Italian and international groups. More recently, activity in this area has moved toward human subjects by focusing on non-invasive EEG-based interfaces. The most recent results concern the characterization of neural correlates of motor learning. The ultimate goal is to use EEG signals as bio-feedback to promote motor skill learning.

Body-machine interfaces
The goal of a body-machine interface (BMI) is to map the residual motor capabilities of a disabled person into control signals that allow to control external devices. This approach aims at facilitating the reorganization of residual motion after disease or injury, through a user specific approach (Mussa-Ivaldi 2011). We have developed a novel body-machine interface based on two key concepts:

  1. remapping the residual motion ability into a low dimensional control space, and
  2. matching this control space to the evolving skills of the user.

The proposed interface establishes a form of continuous mobility that is analogous to the mobility of the natural limbs. This paradigm differs sharply from others previous approaches based upon the recognition of discrete control patterns. In terms of assistive devices and rehabilitation, this kind of BMI allows to re-connect disabled people with external world, through signals derived from the users’ body, rather than replacing or bypassing them. Two learning processes take place while using the interface: users practice the assistive device, and the interface modifies itself based on the user’s residual abilities and preferences. We assessed the feasibility of this method as a way of training and enhancing upper body motor skills in persons with high-level spinal cord injury (SCI) (Casadio 2009, 2010). The work is carried out in collaboration with F.A. Mussa-Ivaldi, Northwestern University and Rehabilitation Institute of Chicago, and with R. Scheidt, Marquette University, Milwaukee.