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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...
Autores principales: | Hübner, David, Verhoeven, Thibault, Schmid, Konstantin, Müller, Klaus-Robert, Tangermann, Michael, Kindermans, Pieter-Jan |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5391120/ https://www.ncbi.nlm.nih.gov/pubmed/28407016 http://dx.doi.org/10.1371/journal.pone.0175856 |
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