Hendrik Wöhrle

Learn More
In neuroscience large amounts of data are recorded to provide insights into cerebral information processing and function. The successful extraction of the relevant signals becomes more and more challenging due to increasing complexities in acquisition techniques and questions addressed. Here, automated signal processing and machine learning tools can help(More)
The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved(More)
This work introduces the architecture of a novel brain-arm haptic interface usable to improve the operation of complex robotic systems, or to deliver a fine rehabilitation therapy to the human upper limb. The proposed control scheme combines different approaches from the areas of robotics, neuroscience and human-machine interaction in order to overcome the(More)
Advanced man-machine interfaces (MMIs) are being developed for teleoperating robots at remote and hardly accessible places. Such MMIs make use of a virtual environment and can therefore make the operator immerse him-/herself into the environment of the robot. In this paper, we present our developed MMI for multi-robot control. Our MMI can adapt to changes(More)
GOAL Current brain-computer interfaces (BCIs) are usually based on various, often supervised, signal processing methods. The disadvantage of supervised methods is the requirement to calibrate them with recently acquired subject-specific training data. Here, we present a novel algorithm for dimensionality reduction (spatial filter), that is ideally suited(More)