Improving zero-training brain-computer interfaces by mixing model estimators

Objective. Brain-computer interfaces (BCI) based on event-related potentials (ERP) incorporate a decoder to classify recorded brain signals and subsequently select a control signal that drives a computer application. Standard supervised BCI decoders require a tedious calibration procedure prior to e...

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Veröffentlicht in:	Journal of neural engineering 2017-06, Vol.14 (3), p.036021-036021
Hauptverfasser:	Verhoeven, T, Hübner, D, Tangermann, M, Müller, K R, Dambre, J, Kindermans, P J
Format:	Artikel
Sprache:	eng
Schlagworte:	Adult Algorithms Brain - physiology brain-computer interface Brain-Computer Interfaces Communication Aids for Disabled Computer Simulation Data Interpretation, Statistical event-related potential Evoked Potentials - physiology Female Humans Machine Learning Male Models, Statistical P300 speller Pattern Recognition, Automated - methods Reproducibility of Results Sensitivity and Specificity Task Performance and Analysis
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