A bayesian model for exploiting application constraints to enable unsupervised training of a P300-based BCI
about
Using neurophysiological signals that reflect cognitive or affective state: six recommendations to avoid common pitfallsLearning from label proportions in brain-computer interfaces: Online unsupervised learning with guaranteesA Synchronous Motor Imagery Based Neural Physiological Paradigm for Brain Computer Interface Speller.Integrating language models into classifiers for BCI communication: a review.True zero-training brain-computer interfacing--an online study.Challenges in clinical applications of brain computer interfaces in individuals with spinal cord injury.Overlapped partitioning for ensemble classifiers of P300-based brain-computer interfacesExploiting Task Constraints for Self-Calibrated Brain-Machine Interface Control Using Error-Related Potentials.Incorporation of Inter-Subject Information to Improve the Accuracy of Subject-Specific P300 Classifiers.
P2860
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P2860
A bayesian model for exploiting application constraints to enable unsupervised training of a P300-based BCI
description
2012 nî lūn-bûn
@nan
2012 թուականի Ապրիլին հրատարակուած գիտական յօդուած
@hyw
2012 թվականի ապրիլին հրատարակված գիտական հոդված
@hy
2012年の論文
@ja
2012年論文
@yue
2012年論文
@zh-hant
2012年論文
@zh-hk
2012年論文
@zh-mo
2012年論文
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2012年论文
@wuu
name
A bayesian model for exploitin ...... d training of a P300-based BCI
@ast
A bayesian model for exploitin ...... d training of a P300-based BCI
@en
type
label
A bayesian model for exploitin ...... d training of a P300-based BCI
@ast
A bayesian model for exploitin ...... d training of a P300-based BCI
@en
prefLabel
A bayesian model for exploitin ...... d training of a P300-based BCI
@ast
A bayesian model for exploitin ...... d training of a P300-based BCI
@en
P2093
P2860
P1433
P1476
A bayesian model for exploitin ...... d training of a P300-based BCI
@en
P2093
Benjamin Schrauwen
David Verstraeten
Pieter-Jan Kindermans
P2860
P304
P356
10.1371/JOURNAL.PONE.0033758
P407
P577
2012-04-04T00:00:00Z