Consortium: Prof. Dr. Ioannis Iossifidis (PI, consortium lead), Dr. Christian Klaes (PI), Ruhr University Bochum-Knappschaft University Hospital, Prof. Dr. Martin Tegenthoff (PI) Ruhr University- Bergmannsheil University Hospital, Dr. Corinna Weber, Snap GmbH (PI))
- Project duration: 01/2020 — 12/2022
- Funding volume: € 2.092.917,-
Restrictions in hand and arm function are a highly relevant consequence of neurological diseases, such as Parkinson’s disease, strokes or spinal cord injuries, and have an enormous impact on the quality of life and participation of affected patients. A differentiated diagnosis of hand and arm function is of great importance both for therapy control and for early detection. At present, however, very different and only conditionally objectifiable test instruments are used for specific indications, which result in an inaccuracy retest and make it difficult to compare results. In addition, relevant biosignals such as EEG and EMG are not used or not used in direct combination with the functional tests.
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, such as deep learning, in the context of diagnosis and therapy of neurological diseases with hand and arm dysfunctions.
The easy-to-use functional test can be used in particular for complex extrapyramidal and/or cerebellar movement disorders in order to objectively classify the movement deficits and compare them with comparative data. Due to the increased sensitivity of the test and a generative model of arm movements, different stages of the disease can be better distinguished and the course of the disease more clearly documented. The early detection of diseases with a gradual course should also be improved. Therapeutically, the insights gained in tremor treatment – here by means of a hand exoskeleton to be developed in the project – will be used to establish VR-supported neurofeedback therapy approaches for extrapyramidal and cerebellar movement disorders and for fine calibration for deep brain stimulation for Parkinson’s treatment.
Movement and correlated EEG data from both healthy volunteers and patients will be used to build a publicly accessible database. The database will be made available as a reference for the development and validation of models and methods for the scientific community and companies. The developed modular hardware and software components will be used clinically / scientifically (VR test environment) as well as economically (test instrument, decoder, hand exoskeleton).