Welcome to KlaesLab

led by Christian Klaes

The KlaesLab specializes in exploring the intersection between biological and artificial systems. Professor Klaes holds the professorship for neurotechnology at the Ruhr-University Bochum. He is an expert in human and monkey brain-computer interfaces, both invasive and non-invasive. Currently, the lab is focused on using state-of-the-art machine learning approaches for brain-machine interfaces to control devices, developing advanced neural analysis methods to control human hand exoskeletons, and conducting virtual reality research. The lab uses VR, EEG, and invasive neural recordings from humans, providing a wide range of research opportunities. The lab has a broad network of collaborations within Ruhr-University Bochum, as well as with external partners such as Caltech, Ruhr University of Applied Sciences (Iossifidis Lab), German Primate Center, University of Coimbra, Snap Gmbh, and many others.

Key research areas

We currently focus on the use of virtual reality in simulating human interactions with environment, brain-machine interfaces in control of devices, collaborative robotics, novel methods for assessment of brain activity and designing advanced neural analysis methods for controlling human hand exoskeleton.

The use of virtual reality for diagnostics of neurological disorders

We develop a standardized test for diagnosing motor function disorders in Virtual Reality (VR). We 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.

Neural signal processing using artificial intelligence on an embedded platform

The aim of this project is to develop an end-to-end neural processing pipeline that implements online algorithms for spike sorting and neural decoding using analogue and digital embedded hardware. This pipeline is a fundamental building block for the development of the next generation of neural implants. The system relies on state-of-the-art machine learning algorithms to decode movement intentions from brain activity. In parallel, we will optimise a neural decoder based on deep learning methods.

Smart upper-limb exoskeleton

We are interested in developing a key component –a bio-mechanically designed, adaptive exoskeleton for the upper extremity — to explore and enable patients with arm and grip function impairment due to various neurological diseases in their journey of rehabilitation. The exoskeleton is a holistic rehabilitation system that includes the design and implementation of movement tasks in VR, a feedback system based on biosignals and a generic decoder for invasive and non-invasive brain-computer interfaces.

Collaborative robotics

Together with Ruhr University for Applied Sciences, University of Coimbra and University of Madeira, we develop a framework for trustful and efficient motor collaboration between humans and robotic agents. We use human biomarkers as a proxy of human state in order to adaptively guide actions of the collaborative robot.

Terahertz

We apply terahertz radiation to discover novel methods for imaging brain activity for future brain-machine interfaces.

If you are interested in our projects and/or working with us,

please visit our project page and reach out to us.

 

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