Team Müller-Putz

Brain-Computer Interface research has developed to a very broad field. It covers the understanding of brain processes, experimental design, recording of brain activity, signal processing and feature extraction. With machine learning methods brain signals can be decoded and translated to control signals for a large variety of applications. Artifact handling is as important as setting potential end users into the center of the resarch.
The main research lines are:
movement decoding | error processing | EEG-based neuroprosthesis control | communication with BCI in patients with disorders of consciousness | hybrid BCI systems | the human somatosensory system | functional brain mapping | BCIs in assistive technology | biosignal analysis and machine learning | Neuro Information Systems research | BCIs for autonomous mobility

Research Topics

Clinical application of BCI-based motor Neuroprosthetics in spinal cord injury

A long history of research in developing brain-computer interfaces for the control of upper-limb neuroprostheses for the restoration of hand and grasping movements in indivuduals with cervical spinal cord injury has been demonstrated. The latest project, funded by the European Commission, MoreGrasp introduced new natuaralistic control strategies for this application.

ERC: Feel Your Reach

This project builds the basis for the next generation of robotic arm control to be used in people with high cervical spinal cord lesions. Non-invasively, we are investigating new methodologies to decode complex arm and hand movements from multi-channel EEG.

Communication for people with Disorders of Consciousness

Communication is one of the most important things for humans. Individuals with neurodegenerative deseases or even in a minimally consciousness state may be completely unable to communicate with other people. The goal of this project is to develop at least a simple communication tool, based on the analysis of brain signals, to allow those persons at least rudimentary communciation with their environment.

Autonomous control through measurement of the neural correlate of perturbation

Passive BCIs can be used applied in many situations in, e.g., for detecting errors during control, measuring mental workload or attention. This project deals with the investigation, understanding and finally the detection of perceived perturbations a human recognizes in several scenarious.

Scheduling 'Eureka' Moments

Can we develop technologies that allow users to let their minds wander to increase the likelihood of "Eureka" or "Aha" moments? Through an experimental approach and triangulation of self-reports and synchronized physiological measures (electroencephalography (EEG) and eye tracking), we seek to answer this question. Our results may have important new implications for the design of digital workplaces and may help to inspire the irreplaceable ability of humans to find innovative solutions.