Data-Driven NeuroTechnology Lab

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.

Machine learning research

We investigate supervised and unsupervised machine learning models capable to:

deal with high noise, high dimensional multi-channel and multi-modal data
embrace invasive and non-invasiv time-series brain signals as well as behavioural information
realize domain-specific, data-driven preprocessing and feature extraction
integrate domain-specific regularization approaches
adapt over time to non-stationary feature distributions
transfer between recording sessions, users and tasks
learn from very small training data sets
learn from unlabeled, but structured data
minimize the model training (calibration) period
learn invariant, robust representations and embedding functions

Neuroscience Research

making use of these machine learning methods, we are trying to gain a better understanding of these topics:

relationship between behaviour and brain activity
relationship between invasive and non-invasive brain signals
understanding the underlying neural mechanisms
1. of successful vs. failing hand motor tasks
2. of language rehabilitation after stroke
3. induced by (adaptive) deep brain stimulation
4. of successful BCI control

Applications

Algorithms developed must prove their feasibility, efficiency and robustness in closed-loop systems for clinical and non-clinical applications such as:

auditory BCI-supported language rehabilitation training of chronic stroke patients with aphasia
controlling adaptive deep brain stimulation (DBS) systems
brain-computer interface (BCI) systems for communication and control, for media- and gaming applications
(Bayesian) optimization of experimental parameters under noisy objective functions
forecasting motor and cognitive performance in repetitive tasks
brain state informed human-robot interaction