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L1MCCAforSSVEP_Demo​.zip

Version 1.1.0.0 (4.75 MB) by Yu Zhang
This code is a demo to show L1MCCA vs CCA for SSVEP recognition.
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Updated 15 Aug 2014

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This code is a demo to show L1-regularized multiway canonical correlation analysis (L1MCCA) can outperform CCA for SSVEP recognition in BCI.
To see the results, you just run the m file titled "L1MCCAforSSVEP_Demo".
For more details, please see the paper:
Y. Zhang, G. Zhou, J. Jin, M. Wang, X. Wang, A. Cichocki. L1-regularized multiway canonical correlation analysis for SSVEP-based BCI. IEEE Trans. Neural Syst. Rehabil. Eng., vol. 21, no. 6, pp. 887-896, 2013.
If you have any question about this code, please do not hesitate to contact me:
zhangyu0112@gmail.com

Cite As

Yu Zhang (2023). L1MCCAforSSVEP_Demo.zip (https://www.mathworks.com/matlabcentral/fileexchange/47496-l1mccaforssvep_demo-zip), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2012a
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L1MCCAforSSVEP_Demo/

Version Published Release Notes
1.1.0.0

Note: This demo requires the tensor_toolbox developed by Kolda that can be download at: (http://www.sandia.gov/~tgkolda/TensorToolbox/index-2.5.html)
1.0.0.0