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si-gcca

Code and experiments for stimulus-informed generalized canonical correlation analysis

https://github.com/alexanderbertrandlab/si-gcca

Science Score: 67.0%

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    CITATION.cff file
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    Found 10 DOI reference(s) in README
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    Links to: ieee.org
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    Low similarity (8.8%) to scientific vocabulary
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Repository

Code and experiments for stimulus-informed generalized canonical correlation analysis

Basic Info
  • Host: GitHub
  • Owner: AlexanderBertrandLab
  • License: other
  • Language: MATLAB
  • Default Branch: main
  • Size: 49.8 KB
Statistics
  • Stars: 6
  • Watchers: 2
  • Forks: 1
  • Open Issues: 0
  • Releases: 0
Created over 3 years ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

README.md

Stimulus-Informed Generalized Canonical Correlation Analysis for Group Analysis of Stimulus-Following Neural Responses

License

See the LICENSE file for license rights and limitations. By downloading and/or installing this software and associated files on your computing system you agree to use the software under the terms and condition as specified in the License agreement.

If this code has been useful for you, please cite [1].

About

This repository includes the MATLAB-code for the (SI-)GCCA algorithms (and corrCA variants) as explained in 1 as well as all the experiments from the paper in [1], conducted on the publicly available dataset of [2]. The experimental files for the video dataset [3] are not available, due to copyright constraints on the video stimuli (see [3]). However, the analysis code is very similar to the group size experiment on the speech data (which is available), such that it is fairly easy to reproduce the results on the video data once the video features are generated.

Developed and tested in MATLAB R2021b.

Note: Tensorlab is required (https://www.tensorlab.net/).

Contact

Simon Geirnaert
KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics
KU Leuven, Department of Neurosciences, Research Group ExpORL
Leuven.AI - KU Leuven institute for AI
simon.geirnaert@esat.kuleuven.be

Yuanyuan Yao KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics
Leuven.AI - KU Leuven institute for AI
yuanyuan.yao@esat.kuleuven.be

Tom Francart KU Leuven, Department of Neurosciences, Research Group ExpORL
Leuven.AI - KU Leuven institute for AI
tom.francart@kuleuven.be

Alexander Bertrand KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics
Leuven.AI - KU Leuven institute for AI
alexander.bertrand@esat.kuleuven.be

## References

[1] S. Geirnaert, Y. Yao, T. Francart and A. Bertrand, "Stimulus-Informed Generalized Canonical Correlation Analysis for Group Analysis of Neural Responses to Natural Stimuli," IEEE Journal of Biomedical and Health Informatics, vol. 29, no. 2, pp. 970-983, 2025, https://doi.org/10.1109/JBHI.2024.3462991.

[2] M. P. Broderick, A. J. Anderson, G. M. Di Liberto, M. J. Crosse, and E. C. Lalor, “Data from: Electrophysiological correlates of semantic dissimilarity reflect the comprehension of natural, narrative speech,” Feb. 2019. [Online]. Available: https://doi.org/10.5061/dryad.070jc

[3] Y. Yao, A. Stebner, T. Tuytelaars, S. Geirnaert, and A. Bertrand, “Video-EEG Encoding-Decoding Dataset KU Leuven,” Zenodo, Jan. 2024. [Online]. Available: https://doi.org/10.5281/zenodo.10512414.

Owner

  • Name: AlexanderBertrandLab
  • Login: AlexanderBertrandLab
  • Kind: organization

Citation (CITATION.cff) 

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: SI-GCCA Toolbox and Experiments
message: >-
  If you use this software, please cite it and the
  corresponding paper: S. Geirnaert, Y. Yao, T. Francart and
  A. Bertrand, "Stimulus-Informed Generalized Canonical
  Correlation Analysis for Group Analysis of Neural
  Responses to Natural Stimuli," arXiv, 2024,
  https://doi.org/10.48550/arXiv.2401.17841.
type: software
authors:
  - given-names: Simon
    family-names: Geirnaert
    email: simon.geirnaert@esat.kuleuven.be
    affiliation: KU Leuven
    orcid: 'https://orcid.org/0000-0002-4120-4232'
  - given-names: Yuanyuan
    family-names: Yao
    email: yuanyuan.yao@esat.kuleuven.be
    affiliation: KU Leuven
  - given-names: Tom
    family-names: Francart
    email: tom.francart@kuleuven.be
    affiliation: KU Leuven
  - given-names: Alexander
    family-names: Bertrand
    email: alexander.bertrand@esat.kuleuven.be
    affiliation: Ku Leuven
identifiers:
  - type: doi
    value: 10.48550/arXiv.2401.17841
repository-code: >-
  https://github.com/AlexanderBertrandLab/si-gcca?tab=readme-ov-file
abstract: >-
  This repository includes the MATLAB-code for the (SI-)GCCA
  algorithms (and corrCA variants) as explained in [1] (in
  the toolbox) as well as all the experiments from the paper
  in [1], conducted on the publicly available dataset of
  [2]. The experimental files for the video dataset [3] are
  not available, due to copyright constraints on the video
  stimuli (see [3]). However, the analysis code is very
  similar to the group size experiment on the speech data
  (which is available), such that it is fairly easy to
  reproduce the results on the video data once the video
  features are generated.


  [1] S. Geirnaert, Y. Yao, T. Francart and A. Bertrand,
  "Stimulus-Informed Generalized Canonical Correlation
  Analysis for Group Analysis of Neural Responses to Natural
  Stimuli," arXiv, 2024,
  https://doi.org/10.48550/arXiv.2401.17841.


  [2] M. P. Broderick, A. J. Anderson, G. M. Di Liberto, M.
  J. Crosse, and E. C. Lalor, “Data from:
  Electrophysiological correlates of semantic dissimilarity
  reflect the comprehension of natural, narrative speech,”
  Feb. 2019. [Online]. Available:
  https://doi.org/10.5061/dryad.070jc


  [3] Y. Yao, A. Stebner, T. Tuytelaars, S. Geirnaert, and
  A. Bertrand, “Video-EEG Encoding-Decoding Dataset KU
  Leuven,” Zenodo, Jan. 15, 2024. doi:
  10.5281/zenodo.10512414.

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