Kinematic and Neurophysiological Consequences of an Assisted-Force-Feedback Brain-Machine Interface Training: A Case Study - Silvoni, Stefano; Cavinato, Marianna; Volpato, Chiara; Cisotto, Giulia; Genna, Clara; Agostini, Michela; Turolla, Andrea; Ramos-Murguialday, Ander; Piccione, Francesco (Frontiers in Neurology, 2013)
In a proof-of-principle prototypical demonstration we describe a new type of brain-machine interface (BMI) paradigm for upper limb motor training. The proposed technique allows a fast contingent and proportionally modulated stimulation of afferent proprioceptive and motor output neural pathways using operant learning. Continuous and immediate assisted-feedback of force proportional to rolandic rhythm oscillations during actual movements was employed and illustrated with a single case experiment. One hemiplegic patient was trained for two weeks coupling somatosensory brain oscillations with force field control during a robot mediated centre-out motor task whose execution approaches movements of everyday life. The robot facilitated actual movements adding a modulated force directed to the target, thus providing a non-delayed proprioceptive feedback. Neuro-electric, kinematic and motor-behavioural measures were recorded in pre- and post-assessments without force assistance. Patient’s healthy arm was used as control since neither a placebo control was possible nor other control conditions. We observed a generalized and significant kinematic improvement in the affected arm and a spatial accuracy improvement in both arms, together with an increase and focalization of the somatosensory rhythm changes used to provide assisted-force-feedback. The interpretation of the neurophysiological and kinematic evidences reported here is strictly related to the repetition of the motor-task and the presence of the assisted-force-feedback. Results are described as systematic observations only, without firm conclusions about the effectiveness of the methodology. In this prototypical view, the design of appropriate control conditions is discussed. This study presents a novel operant-learning-based BMI-application for motor training coupling brain oscillations and force feedback during an actual movement.