Learning from label proportions in brain-computer interfaces: Online unsupervised learning with guarantees

OBJECTIVE: Using traditional approaches, a brain-computer interface (BCI) requires the collection of calibration data for new subjects prior to online use. Calibration time can be reduced or eliminated e.g., by subject-to-subject transfer of a pre-trained classifier or unsupervised adaptive classifi...

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Publicado en:PLoS One
Autores principales: Hübner, David, Verhoeven, Thibault, Schmid, Konstantin, Müller, Klaus-Robert, Tangermann, Michael, Kindermans, Pieter-Jan
Formato: Artigo
Lenguaje:Inglês
Publicado: Public Library of Science 2017
Materias:
Research Article
Acceso en línea:https://ncbi.nlm.nih.gov/pmc/articles/PMC5391120/
https://ncbi.nlm.nih.gov/pubmed/28407016
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0175856
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