A mirror therapy system using virtual reality and an actuated exoskeleton for the recovery of hand motor impairments: a study of acceptability, usability, and embodiment.

Hand motor impairments are one of the main causes of disabilities worldwide. Rehabilitation procedures like mirror therapy are given crucial importance. In the traditional setup, the patient moves the healthy hand in front of a mirror; the view of the mirrored motion tricks the brain into thinking that the impaired hand is moving as well, stimulating the recovery of the lost hand functionalities. We propose an innovative mirror therapy system that leverages and couples cutting-edge technologies. Virtual reality recreates an immersive and effective mirroring effect; a soft hand exoskeleton accompanies the virtual visual perception by physically inducing the mirrored motion to the real hand. Three working modes of our system have been tested with 21 healthy users. The system is ranked as acceptable by the system usability scale; it does not provoke adverse events or sickness in the users, according to the simulator sickness questionnaire; the three execution modes are also compared w.r.t. the sense of embodiment, evaluated through another customized questionnaire. The achieved results show the potential of our system as a clinical tool and reveal its social and economic impact.

Detecting intention to grasp during reaching movements from EEG.

Brain-computer interfaces (BCI) have been shown to be a promising tool in rehabilitation and assistive scenarios. Within these contexts, brain signals can be decoded and used as commands for a robotic device, allowing to translate user's intentions into motor actions in order to support the user's impaired neuro-muscular system. Recently, it has been suggested that slow cortical potentials (SCPs), negative deflections in the electroencephalographic (EEG) signals peaking around one second before the initiation of movements, might be of interest because they offer an accurate time resolution for the provided feedback. Many state-of-the-art studies exploiting SCPs have focused on decoding intention of movements related to walking and arm reaching, but up to now few studies have focused on decoding the intention to grasp, which is of fundamental importance in upper-limb tasks. In this work, we present a technique that exploits EEG to decode grasping correlates during reaching movements. Results obtained with four subjects show the existence of SCPs prior to the execution of grasping movements and how they can be used to classify, with accuracy rates greater than 70% across all subjects, the intention to grasp. Using a sliding window approach, we have also demonstrated how this intention can be decoded on average around 400 ms before the grasp movements for two out of four subjects, and after the onset of grasp itself for the two other subjects.