This code shows the comparison study between the standard canonical correlation analysis (CCA), the Multi-set CCA (MsetCCA), and the Multi-set CCA with sine-cosine reference (MsetCCA-R) for SSVEP recognition. Results show that the MsetCCA-R looks better than the other two methods.
Please refer the following papers for more details:
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Standard CCA: Chen, X., et al. (2015). Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain–computer interface. Journal of neural engineering, 12(4), 046008.
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MsetCCA: Zhang, Y., et al. (2014). Frequency recognition in SSVEP-based BCI using multiset canonical correlation analysis. International journal of neural systems, 24(04), 1450013.
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MsetCCA-R: Wong, C. M., et al. (2020). Spatial filtering in SSVEP-based BCIs: unified framework and new improvements. IEEE Transactions on Biomedical Engineering, 67(11), 3057-3072.
This code is prepared by Chi Man Wong ([email protected]).
10 May 2021 (v1.0): MsetCCA-R
If you use this code for a publication, please cite the following paper:
@article{wong2020spatial,
title={Spatial Filtering in SSVEP-based BCIs: Unified Framework and New Improvements},
author={Wong,Chi Man and Wang, Boyu and Wang, Ze and Lao, Ka Fai and Rosa, Agostinho and Wan, Feng},
title={{S}patial {F}iltering in {SSVEP}-based {BCI}s: {U}nified {F}ramework and {N}ew {I}mprovements},
journal=IEEE Trans. Biomed. Eng.,
volume={67},
number={11},
pages={3057 --3072},
year={2020},
publisher={IEEE}
}
Please email me ([email protected]) if you find any mistakes and problems about it.