Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain–computer interface

2015769 citationsJournal Article

Authors

Yijun Wang · University of California, San Diego

Tzyy‐Ping Jung · University of California, San Diego

Abstract

By incorporating the fundamental and harmonic SSVEP components in target identification, the proposed FBCCA method significantly improves the performance of the SSVEP-based BCI, and thereby facilitates its practical applications such as high-speed spelling.

Topics & Keywords

EEG and Brain-Computer Interfaces Neuroscience and Neural Engineering Neural dynamics and brain function

Publication Details

Published in: Journal of Neural Engineering

Volume 12, Issue 4, pp. 046008-046008

DOI: 10.1088/1741-2560/12/4/046008

Field-Weighted Citation Impact: 13.24

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Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain–computer interface

Authors

Abstract

Topics & Keywords

Publication Details