The lab is embedded in the Donders Institute for Brain, Cognition and Behaviour and in the department of Artificial Intelligence. Our research focusses on data-driven neurotechnology, i.e., domain-specific machine learning methods which allow to decode brain states in single trial and methods which permit to modulate the ongoing brain state.
The developed methods are employed to further our insight into perception-, motor-, and cognitive functions for both, the healthy and the diseased brain.
Building upon these insights we regularly transfer machine learning models into closed loop protocols (1) to provide novel tools for the fundamental neuroscientific research, and (2) to create applications such as brain-computer interface (BCI) systems for patients and healthy users.
We investigate supervised and unsupervised machine learning models capable to:
making use of these machine learning methods, we are trying to gain a better understanding of these topics:
Algorithms developed must prove their feasibility, efficiency and robustness in closed-loop systems for clinical and non-clinical applications such as:
We collaborate with these partners for research and education: