Data-Driven NeuroTechnology lab
Data-Driven NeuroTechnology lab
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Identifying Good Donor Datasets for Transfer Learning Scenarios in Motor Imagery BCI
Pierre Guetschel
,
Michael Tangermann
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DOI
Approximate UMAP allows for high-rate online visualization of high-dimensional data streams
Peter Wassenaar
,
Pierre Guetschel
,
Michael Tangermann
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DOI
URL
S-JEPA: towards seamless cross-dataset transfer through dynamic spatial attention
Pierre Guetschel
,
Thomas Moreau
,
Michael Tangermann
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DOI
URL
Synthesizing EEG Signals from Event-Related Potential Paradigms with Conditional Diffusion Models
Guido Klein
,
Pierre Guetschel
,
Gianluigi Silvestri
,
Michael Tangermann
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DOI
URL
A comparison of stimulus sequences for code-modulated visual evoked potential (c-VEP) based BCI
Jordy Thielen
,
Gijs Cornielje
,
Floris van der Werff
,
Peter Desain
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DOI
EEG potentials evoked by deep brain stimulation in patients with treatment-resistant depression
Joana Pereira
,
Matthias Dold
,
Bastian Sajonz
,
Michael Tangermann
,
Volker A. Coenen
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DOI
Neural network transfer learning with fast calibration for mental imagery decoding
Pierre Guetschel
,
Théodore Papadopoulo
,
Michael Tangermann
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DOI
Platform for closed-loop deep brain stimulation research: DAREPLANE
Matthias Dold
,
Joana Pereira
,
Bastian Sajonz
,
Volker A. Coenen
,
Mark L. F. Janssen
,
Michael Tangermann
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DOI
The influence of pitch modulation on the performance of a BCI-based language training system
Simon Kojima
,
Mariacristina Musso
,
Crispijn Aalberts
,
Benjamin E. Kortenbach
,
Sara Miloševska
,
Kim de Wit
,
Shin-Shiro Kanoh
,
Michael Tangermann
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DOI
UMM: Unsupervised Classification of ERPs with Confidence
Michael Tangermann
,
Jan Sosulski
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DOI
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