A New CCA-Based Method for Improving SSVEP-Based BCI System Classification
Author:
Affiliation:
1. Zhengzhou University of Light Industry,College of Electrical and Information Engineering,Zhengzhou,China,450000
2. Henan Technical Institue,Zhengzhou,China,450000
Funder
National Natural Science Foundation of China
Zhengzhou University
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10261767/10261776/10261825.pdf?arnumber=10261825
Reference22 articles.
1. A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials
2. Brain-Computer Interface Based on Classification of Statistical and Power Spectral Density Features[J];mina;Biomedical Engineering,2006
3. Multiway Canonical Correlation Analysis for Frequency Components Recognition in SSVEP-Based BCIs
4. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain–computer interface
5. An Efficient Frequency Recognition Method Based on Likelihood Ratio Test for SSVEP-Based BCI
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