44 citations · 50 across the 9 of their papers we have counts for
9 papers
Interpretable and Granular Video-Based Quantification of Motor Characteristics from the Finger Tapping Test in Parkinson Disease
Tahereh Zarrat Ehsan, Michael Tangermann, Yağmur Güçlütürk +2
Accurately quantifying motor characteristics in Parkinson disease (PD) is crucial for monitoring disease progression and optimizing treatment strategies. The finger-tapping test is…
UMM: Unsupervised Mean-difference Maximization
Jan Sosulski, Michael Tangermann
Many brain-computer interfaces make use of brain signals that are elicited in response to a visual, auditory or tactile stimulus, so-called event-related potentials (ERPs). In visu…
An embedding for EEG signals learned using a triplet loss
Pierre Guetschel, Théodore Papadopoulo, Michael Tangermann
Neurophysiological time series recordings like the electroencephalogram (EEG) or local field potentials are obtained from multiple sensors. They can be decoded by machine learning…
Introducing Block-Toeplitz Covariance Matrices to Remaster Linear Discriminant Analysis for Event-related Potential Brain-computer Interfaces
Jan Sosulski, Michael Tangermann
Covariance matrices of noisy multichannel electroencephalogram time series data are hard to estimate due to high dimensionality. In brain-computer interfaces (BCI) based on event-r…
Online Optimization of Stimulation Speed in an Auditory Brain-Computer Interface under Time Constraints
Jan Sosulski, David Hübner, Aaron Klein +1
The decoding of brain signals recorded via, e.g., an electroencephalogram, using machine learning is key to brain-computer interfaces (BCIs). Stimulation parameters or other experi…
Learning User Preferences for Trajectories from Brain Signals
Henrich Kolkhorst, Wolfram Burgard, Michael Tangermann
Robot motions in the presence of humans should not only be feasible and safe, but also conform to human preferences. This, however, requires user feedback on the robot's behavior.…