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Automated classification of electroencephalogram (EEG) signals is complex due to their high dimensionality, non-stationarity, low signal-to-noise ratio, and variability between subjects. Deep neural networks (DNNs) have shown promising results for EE
Externí odkaz:
http://arxiv.org/abs/2405.14994
Autor:
Carrara, Igor, Aristimunha, Bruno, Corsi, Marie-Constance, de Camargo, Raphael Y., Chevallier, Sylvain, Papadopoulo, Théodore
Objective: The integration of Deep Learning (DL) algorithms on brain signal analysis is still in its nascent stages compared to their success in fields like Computer Vision. This is particularly true for BCI, where the brain activity is decoded to co
Externí odkaz:
http://arxiv.org/abs/2403.05645
Electroencephalography (EEG) signals are frequently used for various Brain-Computer Interface (BCI) tasks. While Deep Learning (DL) techniques have shown promising results, they are hindered by the substantial data requirements. By leveraging data fr
Externí odkaz:
http://arxiv.org/abs/2401.10746
Autor:
Aristimunha, Bruno, de Camargo, Raphael Y., Pinaya, Walter H. Lopez, Chevallier, Sylvain, Gramfort, Alexandre, Rommel, Cedric
Electroencephalography (EEG) decoding is a challenging task due to the limited availability of labelled data. While transfer learning is a promising technique to address this challenge, it assumes that transferable data domains and task are known, wh
Externí odkaz:
http://arxiv.org/abs/2308.02408
Healthcare AI holds the potential to increase patient safety, augment efficiency and improve patient outcomes, yet research is often limited by data access, cohort curation, and tooling for analysis. Collection and translation of electronic health re
Externí odkaz:
http://arxiv.org/abs/2112.06883
Autor:
Cohen, Raphael Y., Sodickson, Aaron D.
Current AI-driven research in radiology requires resources and expertise that are often inaccessible to small and resource-limited labs. The clinicians who are able to participate in AI research are frequently well-funded, well-staffed, and either ha
Externí odkaz:
http://arxiv.org/abs/2107.04409
Publikováno v:
In Journal of Parallel and Distributed Computing June 2022 164:83-95
Akademický článek
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Publikováno v:
Network Neuroscience, Vol 5, Iss 4, Pp 874-889 (2021)
AbstractInferring the structural connectivity from electrophysiological measurements is a fundamental challenge in systems neuroscience. Directed functional connectivity measures, such as the generalized partial directed coherence (GPDC), provide est
Externí odkaz:
https://doaj.org/article/dd67e1e5a5a94add887ff31bbf05e39c
Autor:
Harvey, Veronica Schmidt, author, Prager, Raphael Y., author
Publikováno v:
The Age of Agility : Building Learning Agile Leaders and Organizations, 2021, ill.
Externí odkaz:
https://doi.org/10.1093/oso/9780190085353.003.0006