SFB 874 A11 The Relationship between Implicit and Explicit Visuomotor Task Learning in Hippocampus and Parietal Cortex

led by Christian Klaes and Nicolai Axmacher

Visuomotor tasks, such as motor sequence learning and motor adaptation, depend on various brain structures such as the hippocampus and the parietal cortex. These structures’ involvement might vary depending on the learning stage and on the explicitness of the task.

In this project, we are interested in investigating how brain structures such as the hippocampus and the parietal cortex are involved in implicit versus explicit forms of motor learning and across learning stages. More information about this project can be found here.

Mercur Neural Signal Processing Using Artificial Intelligence on an Embedded Platform

led by Kasten Seidl, Gregor Schiele, Christian Klaes

The goal of this project is to develop an end-to-end neural processing chain that implements online algorithms for spike sorting and neural decoding with analog and digital embedded hardware. This pipeline is a fundamental building block for the development of the next generation of neural implants.

Existing solutions do not allow accurate, fast and energy-efficient interpretation of brain signals. In the project we will define a common augmented dataset for embedded neural processing and develop hardware-based solutions for the different phases of spike sorting. We will optimise a neural decoder based on deep learning methods as well as integrate all parts into a demonstrator and evaluate system performance.

VAFES Motion Trackers and VR for the Diagnosis and Therapy of Neurological Diseases with Hand and Arm Dysfunctions

led by Christian Klaes, Ioannis Iossifidis, Corinna Weber, Mattias Szczesny-Kaiser

Restrictions in hand and arm function as a result of neurological diseases require
differentiated diagnosis both for therapy control and for early detection.

In this project a standardized test environment in Virtual Reality (VR) will be developed. Motion trackers and VR gloves cover a broad spectrum of relevant motion parameters. In particular, synchronous electroencephalographic (EEG) recordings supplement the motion data with neuronal signals. This combination makes it possible for the
first time to use modern Machine Learning (ML) algorithms in the context of diagnosis and therapy of neurological diseases with hand and arm dysfunctions.

REXO Smart Upper Limb Rehabilitation through an Intelligent Soft Exoskeleton

led by Ioannis Iossifidis

Impairment of arm and gripping functions after various neurological diseases limit the participation of affected patients in professional and everyday life. This poses a great challenge to the rehabilitation process. It is necessary to make use of independent and everyday training to foster conventional therapy and facilitate rehabilitation.

This project focuses on the design of a key component –a biomechanically designed, adaptive exoskeleton for the upper extremity –to explore and enable patients in their journey of rehabilitation.