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pro vyhledávání: '"Mellot, Apolline"'
Autor:
Mellot, Apolline, Collas, Antoine, Chevallier, Sylvain, Gramfort, Alexandre, Engemann, Denis A.
Electroencephalography (EEG) data is often collected from diverse contexts involving different populations and EEG devices. This variability can induce distribution shifts in the data $X$ and in the biomedical variables of interest $y$, thus limiting
Externí odkaz:
http://arxiv.org/abs/2407.03878
Combining electroencephalogram (EEG) datasets for supervised machine learning (ML) is challenging due to session, subject, and device variability. ML algorithms typically require identical features at train and test time, complicating analysis due to
Externí odkaz:
http://arxiv.org/abs/2403.15415
Autor:
Engemann, Denis A., Mellot, Apolline, Höchenberger, Richard, Banville, Hubert, Sabbagh, David, Gemein, Lukas, Ball, Tonio, Gramfort, Alexandre
Publikováno v:
In NeuroImage 15 November 2022 262
Publikováno v:
EUSIPCO 2023-The 31st European Signal Processing Conference
EUSIPCO 2023-The 31st European Signal Processing Conference, Sep 2023, Helsinki, Finland
EUSIPCO 2023-The 31st European Signal Processing Conference, Sep 2023, Helsinki, Finland
International audience; Accurate classification of cognitive states from Electroencephalographic (EEG) signals is crucial in neuroscience applications such as Brain-Computer Interfaces (BCIs). Classification pipelines based on Riemannian geometry are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::665c12a5428ee1830b65e181f2a2afc4
https://hal.science/hal-04131609
https://hal.science/hal-04131609