Latent Common Source Extraction (LCSE)
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
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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 (2026). Latent Common Source Extraction (LCSE) (https://github.com/Kiran-Kumar-GR/LCSE), GitHub. Retrieved .
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.
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.
General Information
- Version 1.0.2 (12.2 KB)
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View License on GitHub
MATLAB Release Compatibility
- Compatible with R2015b to R2019b
Platform Compatibility
- Windows
- macOS
- Linux
Versions that use the GitHub default branch cannot be downloaded
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 1.0.2 | Links to the associated articles added in the description. |
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| 1.0.1 | Data Link added |
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| 1.0.0 |
