Zobrazeno 1 - 10
of 244
pro vyhledávání: '"Tangermann, Michael"'
Autor:
Dold, Matthias, Pereira, Joana, Sajonz, Bastian, Coenen, Volker A., Janssen, Marcus L. F., Tangermann, Michael
This work introduces Dareplane, a modular and broad technology agnostic open source software platform for brain-computer interface research with an application focus on adaptive deep brain stimulation (aDBS). While the search for suitable biomarkers
Externí odkaz:
http://arxiv.org/abs/2408.01242
Publikováno v:
Journal of Neural Engineering (2024)
In the field of brain-computer interfaces (BCIs), the potential for leveraging deep learning techniques for representing electroencephalogram (EEG) signals has gained substantial interest. This review synthesizes empirical findings from a collection
Externí odkaz:
http://arxiv.org/abs/2405.19345
Publikováno v:
9th Graz Brain-Computer Interface Conference (2024) 349-354
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 noise level and dimensionality of the signals. Deep neural networks are
Externí odkaz:
http://arxiv.org/abs/2404.04001
Publikováno v:
9th Graz Brain-Computer Interface Conference (2024) 438-443
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 been successfully applied to electroencephalogram (EEG) data, existing m
Externí odkaz:
http://arxiv.org/abs/2403.18486
Publikováno v:
9th Graz Brain-Computer Interface Conference (2024) 11-16
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 Architectures (JEPAs). In recent years, self-supervised learning has emerged a
Externí odkaz:
http://arxiv.org/abs/2403.11772
Autor:
Sosulski, Jan, Tangermann, Michael
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 visual ERP speller applications, sets of letters shown on a screen are fla
Externí odkaz:
http://arxiv.org/abs/2306.11830
Neurophysiological time series recordings like the electroencephalogram (EEG) or local field potentials are obtained from multiple sensors. They can be decoded by machine learning models in order to estimate the ongoing brain state of a patient or he
Externí odkaz:
http://arxiv.org/abs/2304.06495
Autor:
Sosulski, Jan, Tangermann, Michael
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-related potentials and a linear discriminant analysis (LDA) for classif
Externí odkaz:
http://arxiv.org/abs/2202.02001
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 experimental settings of the BCI protocol typically are chosen according to
Externí odkaz:
http://arxiv.org/abs/2109.06011
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. In this work, we propose a novel approach to leverage the user's brain
Externí odkaz:
http://arxiv.org/abs/1909.01039