A Post-Stroke Rehabilitation System Integrating Robotics, VR and High-Resolution EEG Imaging

Abstract

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.

Novel Multipin Electrode Cap System for Dry Electroencephalography

“…Due to improved electronics and sensor concepts various new fields of applications have recently been introduced, including brain computer interfaces (Daly and Wolpaw 2008;Nicolas-Alonso and Gomez-Gil 2012;Hwang et al 2013), ambient assisted living (D'Angelo et al 2010;Askamp and van Putten 2014), EEG analysis during professional sports (Thompson et al 2008;di Fronso et al 2015), rehabilitation (Daly and Wolpaw 2008;Steinisch et al 2013) as well as mobile, ubiquitous and long-term monitoring (Jordan 2004;Debener et al 2012;De Vos and Debener 2014;Askamp and van Putten 2014;Michel et al 2015). However, these applications often require multichannel setups in order to facilitate comprehensive signal processing, analysis and interpretation.…”

Brain-Machine Interfaces for Assistance and Rehabilitation of People with Reduced Mobility

“…This ability allows neurons (nerve cells) to compensate for injuries and diseases, adjusting their activities in response to new situations or changes in their environment. Currently, multiple studies have been conducted focused on the development of this type of rehabilitation systems [18][19][20]. In this regard, brain-machine interfaces (BMIs) can lead to a significant improvement in rehabilitation systems for patients who have suffered a stroke, making them an active part of their own rehabilitation process and thus achieving not only a rehabilitation at muscle level but also an improvement at neurological level, since it is the patients themselves who generate the control commands of the system [21].…”

Design of ELISE robot for the paretic upper limb of stroke survivors

“…This module was prepared for diagnostics of the patient, to define new rehabilitation exercises and to analyze the results (reports) [33,34]. Patient module was prepared to conduct rehabilitation exercises in virtual reality [35][36][37][38][39][40], to supply the virtual assistant for the therapist and to easily extract information from reports. Detailed information about these modules are described in the second part of this chapter.…”