A Bayesian Model for Exploiting Application Constraints to Enable Unsupervised Training of a P300-based BCI

This work introduces a novel classifier for a P300-based speller, which, contrary to common methods, can be trained entirely unsupervisedly using an Expectation Maximization approach, eliminating the need for costly dataset collection or tedious calibration sessions. We use publicly available datase...

Descripción completa

Detalles Bibliográficos
Autores principales: Kindermans, Pieter-Jan, Verstraeten, David, Schrauwen, Benjamin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3319551/
https://www.ncbi.nlm.nih.gov/pubmed/22496763
http://dx.doi.org/10.1371/journal.pone.0033758

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3319551/
https://www.ncbi.nlm.nih.gov/pubmed/22496763
http://dx.doi.org/10.1371/journal.pone.0033758

Ejemplares similares