Latent Common Source Extraction (LCSE)

Version 1.0.2 (12.2 KB) by KIRAN KUMAR RAVINDRAN

This is a sample code for testing Latent Common Source Extraction as described in the article, doi: https://doi.org/10.1088/1741-2552/ab13d1

https://github.com/Kiran-Kumar-GR/LCSE

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Updated 9 Jun 2020

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Latent Common Source Extraction (LCSE) seeks to construct a common latent representation of the SSVEP signal subspace that is stable across multiple trials of EEG data.

The spatial filter thus obtained improves the signal-to-noise ratio (SNR) of the SSVEP components by removing nuisance signals that are irrelevant to the generalized signal representation learnt from the given data.

This is a sample code for testing LCSE as described in the article,

-> Kiran Kumar G. R and Ramasubbareddy M, "Latent common source extraction via a generalized canonical correlation framework for frequency recognition in SSVEP based brain–computer interfaces," in Journal of Neural Engineering. doi: https://doi.org/10.1088/1741-2552/ab13d1

-> G. R. K. Kumar and M. R. Reddy, "Multiview MAX-VAR canonical correlation approach for enhancing SSVEP based BCIs," 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Chicago, IL, USA, 2019, pp. 1-4. doi: 10.1109/BHI.2019.8834650

Note: Use the SSVEP benchmark dataset placed in a folder in the same directory as this file

Link : Link: ftp://sccn.ucsd.edu/pub/ssvep_benchmark_dataset/

Cite As

KIRAN KUMAR RAVINDRAN (2025). Latent Common Source Extraction (LCSE) (https://github.com/Kiran-Kumar-GR/LCSE), GitHub. Retrieved October 28, 2025.

Kumar, G. R. Kiran, and M. Ramasubba Reddy. “Latent Common Source Extraction via a Generalized Canonical Correlation Framework for Frequency Recognition in SSVEP Based Brain–Computer Interfaces.” Journal of Neural Engineering, vol. 16, no. 4, IOP Publishing, May 2019, p. 046004, doi:10.1088/1741-2552/ab13d1.

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Kumar, G. R. Kiran, and M. Ramasubba Reddy. “Multiview MAX-VAR Canonical Correlation Approach for Enhancing SSVEP Based BCIs.” 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), IEEE, 2019, doi:10.1109/bhi.2019.8834650.

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MATLAB Release Compatibility

Created with R2019b

Compatible with R2015b to R2019b

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LCSE_example.m

LCSE_complete

Versions that use the GitHub default branch cannot be downloaded

Version	Published	Release Notes
1.0.2	9 Jun 2020	Links to the associated articles added in the description.
1.0.1	3 Nov 2019	Data Link added
1.0.0	1 Nov 2019

To view or report issues in this GitHub add-on, visit the GitHub Repository.

MLA	Kumar, G. R. Kiran, and M. Ramasubba Reddy. “Latent Common Source Extraction via a Generalized Canonical Correlation Framework for Frequency Recognition in SSVEP Based Brain–Computer Interfaces.” Journal of Neural Engineering, vol. 16, no. 4, IOP Publishing, May 2019, p. 046004, doi:10.1088/1741-2552/ab13d1.
APA	Kumar, G. R. K., & Reddy, M. R. (2019). Latent common source extraction via a generalized canonical correlation framework for frequency recognition in SSVEP based brain–computer interfaces. Journal of Neural Engineering, 16(4), 046004. IOP Publishing. Retrieved from https://doi.org/10.1088%2F1741-2552%2Fab13d1
BibTeX	@article{Kiran_Kumar_2019, doi = {10.1088/1741-2552/ab13d1}, url = {https://doi.org/10.1088%2F1741-2552%2Fab13d1}, year = 2019, month = {may}, publisher = {{IOP} Publishing}, volume = {16}, number = {4}, pages = {046004}, author = {G R Kiran Kumar and M Ramasubba Reddy}, title = {Latent common source extraction via a generalized canonical correlation framework for frequency recognition in {SSVEP} based brain{\textendash}computer interfaces}, journal = {Journal of Neural Engineering} }

MLA	Kumar, G. R. Kiran, and M. Ramasubba Reddy. “Multiview MAX-VAR Canonical Correlation Approach for Enhancing SSVEP Based BCIs.” 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), IEEE, 2019, doi:10.1109/bhi.2019.8834650.
APA	Kumar, G. R. K., & Reddy, M. R. (2019). Multiview MAX-VAR canonical correlation approach for enhancing SSVEP based BCIs. In 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). IEEE. Retrieved from https://doi.org/10.1109%2Fbhi.2019.8834650
BibTeX	@inproceedings{Kumar_2019, doi = {10.1109/bhi.2019.8834650}, url = {https://doi.org/10.1109%2Fbhi.2019.8834650}, year = 2019, month = {may}, publisher = {{IEEE}}, author = {G R Kiran Kumar and M Ramasubba Reddy}, title = {Multiview {MAX}-{VAR} canonical correlation approach for enhancing {SSVEP} based {BCIs}}, booktitle = {2019 {IEEE} {EMBS} International Conference on Biomedical {\&} Health Informatics ({BHI})} }