Stimulus-Informed Generalized Canonical Correlation Analysis for Group Analysis of Stimulus-Following Neural Responses
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If this code has been useful for you, please cite [1].
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.
Developed and tested in MATLAB R2021b.
Note: Tensorlab is required (https://www.tensorlab.net/).
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
[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. 2024. [Online]. Available: https://doi.org/10.5281/zenodo.10512414.