A post-stroke rehabilitation system integrating robotics, VR and high-resolution EEG imaging.

We propose a system for the neuro-motor rehabilitation of upper limbs in stroke survivors. The system is composed of a passive robotic device (Trackhold) for kinematic tracking and gravity compensation, five dedicated virtual reality (VR) applications for training of distinct movement patterns, and high-resolution EEG for synchronous monitoring of cortical activity. In contrast to active devices, the Trackhold omits actuators for increased patient safety and acceptance levels, and for reduced complexity and costs. VR applications present all relevant information for task execution as easy-to-understand graphics that do not need any written or verbal instructions. High-resolution electroencephalography (HR-EEG) is synchronized with kinematic data acquisition, allowing for the epoching of EEG signals on the basis of movement-related temporal events. Two healthy volunteers participated in a feasibility study and performed a protocol suggested for the rehabilitation of post-stroke patients. Kinematic data were analyzed by means of in-house code. Open source packages (EEGLAB, SPM, and GMAC) and in-house code were used to process the neurological data. Results from kinematic and EEG data analysis are in line with knowledge from currently available literature and theoretical predictions, and demonstrate the feasibility and potential usefulness of the proposed rehabilitation system to monitor neuro-motor recovery.