2017 - 2020

Adaptive and highly motivating rehabilitation platform for stroke patients with arm palsy (AMoRSA)

The goal of the project is the creation of an intelligent and comprehensive rehabilitation environment for paralysis after stroke. The central element of the system is a Brain-Machine interface that is used to steer a robotic exoskeleton that is attached to the patient's arm. The feedback of the movement reactivates broken links from brain to muscles. The rehabilitation program is embedded in a Serious Game that motivates patients and enables tracking of the patient's progress.

MotorBIC

2015 - 2018

Implantable, Bidirectional Brain-Computer-Interface for the Restoration of Motor Functions (MOTOR-BIC)

MotorBIC is a collaborative research project conducted together with the University of Freiburg, the University of Ulm, the University Medical Center Freiburg, Cortec Neuro GmbH and Max-Planck-Institute for intelligent Systems Tübingen. The interdisciplinary consortium works towards clinical applications of bidirectional Brain-Computer-Interfaces. Hardware and software development by the universities and by CorTec is complemented by preclinical research and pilot clinical studies. At the Medical Center Tübingen, applications of closed-loop interventions using neural interfacing for stroke rehabilitation are explored.

GRUENS

2013-2016

Gehirn-Roboter Schnittstelle zur motorischen Rehabilitation von Schlaganfall Patienten

In the past years Brain-Machine interfaces (BMI) were used to facilitate control of robotic rehabilitative devices by the brain activity of the sensorimotor cortex. With this methodology motor improvements could be induced in chronically impaired stroke patients. Especially the combination of BMI and physiotherapy proved to be effective. BMI circumvents the dysfunctional efferent pathways from the brain to the muscles and physiotherapy helps to elicit neuroplastic processes that promote motor rehabilitation.

The GRUENS project aims at improving the outcome and accelerating the rehabilitation process. Towards this goal we develop a hybrid decoder that detects the patients' brain signals (Electroencephalography - EEG) and their residual muscle activity (Electromyography - EMG). The decoder generates a combined control signal for a rehabilitation robot, which will allow for the development of more interesting and motivating robot-based rehabilitation schemes for stroke patients. Moreover, direct training of functional movements necessary for daily living will be possible.