Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Guetschel, Pierre"'
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
Autor:
Chevallier, Sylvain, Carrara, Igor, Aristimunha, Bruno, Guetschel, Pierre, Sedlar, Sara, Lopes, Bruna, Velut, Sebastien, Khazem, Salim, Moreau, Thomas
Objective. This study conduct an extensive Brain-computer interfaces (BCI) reproducibility analysis on open electroencephalography datasets, aiming to assess existing solutions and establish open and reproducible benchmarks for effective comparison w
Externí odkaz:
http://arxiv.org/abs/2404.15319
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
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
Publikováno v:
Journal of Neural Engineering; Dec2024, Vol. 21 Issue 6, p1-20, 20p
Autor:
Diaz-Arce, Daniel, Ghouma, Anis, Guetschel, Pierre, Papadopoulo, Théodore, Mantegazza, Massimo, Duprat, Fabrice
Publikováno v:
Congrès Labex ICST
Congrès Labex ICST, Nov 2021, Nantes, France
Congrès Labex ICST, Nov 2021, Nantes, France
International audience
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::76dbcca8b8f23e9643ed090870754776
https://inria.hal.science/hal-03540822
https://inria.hal.science/hal-03540822
Publikováno v:
Soph.IA
Soph.IA, Nov 2020, Sophia Antipolis, France
Soph.IA, Nov 2020, Sophia Antipolis, France
International audience; Epilepsy is a neurological disorder that manifests itself as episodes od epileptic seizure characterized by an unusually sporadic neural activity observable by EEG. A model of transgenic mouse affected by epilepsy has been dev
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::1930e898fc40878087a63223e58b934a
https://hal.inria.fr/hal-03381680/file/poster.pdf
https://hal.inria.fr/hal-03381680/file/poster.pdf