Improving zero-training brain-computer interfaces by mixing model estimators.
Journal:
Journal of neural engineering
Published Date:
Mar 13, 2017
Abstract
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 every session. Several unsupervised classification methods have been proposed that tune the decoder during actual use and as such omit this calibration. Each of these methods has its own strengths and weaknesses. Our aim is to improve overall accuracy of ERP-based BCIs without calibration.
Authors
Keywords
Adult
Algorithms
Brain
Brain-Computer Interfaces
Communication Devices for People with Disabilities
Computer Simulation
Data Interpretation, Statistical
Evoked Potentials
Female
Humans
Machine Learning
Male
Models, Statistical
Pattern Recognition, Automated
Reproducibility of Results
Sensitivity and Specificity
Task Performance and Analysis