KlaesLab - Neurotechnology at Ruhr-University Bochum

Welcome to
KlaesLab

Led by Professor 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 and 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 using VR, EEG, and invasive neural recordings from humans.

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 Madeira, Snap Gmbh, and many others.

Invasive BCI
Non-Invasive BCI
Virtual Reality
KlaesLab Team

The KlaesLab Team

Ruhr-University Bochum

News and Events

KlaesLab - Key Research Areas

Our Focus

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.

MindMove System
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MindMove: Automated Neurorehabilitation

Together with medica Medizintechnik GmbH, Ambulanticum GmbH, SNAP GmbH, and the RUB Chair for Production Systems , we are developing a novel, automated rehabilitation system for grasping therapy that combines modern neurotechnology with robotics. We utilize Brain-Computer Interfaces (BCIs) to detect movement intentions directly from brain activity and synchronize them with robot-assisted movement execution. Supported by machine learning and automated assessments, the project aims to create personalized, high-frequency training scenarios to improve independence for patients with neurological conditions

Neurorehab BCI
BrainGuard Framework
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BrainGuard: Neuro-Cybersecurity

Together with the RUB Chair for Secure Mobile Networking, Physec GmbH, and SNAP Discovery AG, we are developing a systematic neuro-cybersecurity framework for Brain-Computer Interfaces (BCI) and neuromodulatory implants. We aim to create novel security concepts—ranging from neural identification to hardware protection—that safeguard sensitive neuronal data, prevent manipulation, and ensure user autonomy against emerging cyber threats.

Cybersecurity Implants
VR Diagnostics
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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.

VR Deep Learning
Neural Signal Processing
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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.

Embedded AI
Smart Exoskeleton
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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.

Robotics Rehab
Collaborative Robotics
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Collaborative robotics

Together with Ruhr University for Applied Sciences, RUB Chair for Production Systems, 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.

HRI Biomarkers
Terahertz Imaging
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Terahertz

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

Imaging Future Tech
Phantom Touch Research
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Phantom Touch

This line of research explores phantom touch illusion —the perception of touch without physical contact in virtual reality. This can be useful for neurorehabilitation and sensory augmentation. By integrating VR, brain-computer interfaces, and haptic technologies, we aim to enhance motor recovery, rewire neural pathways, and improve sensory experiences for patients with neurological disorders.

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

please reach out to us.

 

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