A new multivariate empirical mode decomposition method for improving the performance of SSVEP-based brain–computer interface-Reference-Cited by-同舟云学术

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A new multivariate empirical mode decomposition method for improving the performance of SSVEP-based brain–computer interface

Published:2017-06-21 Issue:4 Volume:14 Page:046028
ISSN:1741-2560
Container-title:Journal of Neural Engineering
language:
Short-container-title:J. Neural Eng.

Author:

Chen Yi-Feng,Atal Kiran,Xie Sheng-Quan,Liu Quan

Funder

Lottery Health Research Council of New Zealand

Faculty of Engineering Research and Development Grant from the University of Auckland

National Natural Science Foundation of China

Publisher

IOP Publishing

Subject

Cellular and Molecular Neuroscience,Biomedical Engineering

Link

http://iopscience.iop.org/article/10.1088/1741-2552/aa6a23/pdf

Reference46 articles.

1. Brain–computer interfaces for communication and rehabilitation

2. Prediction of Auditory and Visual P300 Brain-Computer Interface Aptitude

3. Using the detectability index to predict P300 speller performance

4. Robust frequency recognition for SSVEP-based BCI with temporally local multivariate synchronization index

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