12 citations · 22 across the 7 of their papers we have counts for
5 papers · 1 filter
Review of Deep Representation Learning Techniques for Brain-Computer Interfaces and Recommendations
Pierre Guetschel, Sara Ahmadi, Michael Tangermann
In the field of brain-computer interfaces (BCIs), the potential for leveraging deep learning techniques for representing electroencephalogram (EEG) signals has gained substantial i…
Approximate UMAP allows for high-rate online visualization of high-dimensional data streams
Peter Wassenaar, Pierre Guetschel, Michael Tangermann
In the BCI field, introspection and interpretation of brain signals are desired for providing feedback or to guide rapid paradigm prototyping but are challenging due to the high no…
Synthesizing EEG Signals from Event-Related Potential Paradigms with Conditional Diffusion Models
Guido Klein, Pierre Guetschel, Gianluigi Silvestri +1
Data scarcity in the brain-computer interface field can be alleviated through the use of generative models, specifically diffusion models. While diffusion models have previously be…
Towards auditory attention decoding with noise-tagging: A pilot study
H. A. Scheppink, S. Ahmadi, P. Desain +2
Auditory attention decoding (AAD) aims to extract from brain activity the attended speaker amidst candidate speakers, offering promising applications for neuro-steered hearing devi…
S-JEPA: towards seamless cross-dataset transfer through dynamic spatial attention
Pierre Guetschel, Thomas Moreau, Michael Tangermann
Motivated by the challenge of seamless cross-dataset transfer in EEG signal processing, this article presents an exploratory study on the use of Joint Embedding Predictive Architec…