This repo includes the code to demonstrate:
- Random matrix theory (RMT) - rmt_demo_forFig1A.m
- RMT based PCA denoising algorithm comparison on a simulated fMRI matrix with and without variance stabilization transformation (VST) used to correct Rician bias in low-SNR magnitude data - ssvd_denoiseTest_forFig4.m
- RMT based PCA denoising on a sample magnitude fMRI data - fmriDenoising_demo.m
If you think this work helps in your project, please consider to cite:
"W. Zhu, X. Ma, X. -H. Zhu, K. Ugurbil, W. Chen and X. Wu, "Denoise Functional Magnetic Resonance Imaging with Random Matrix Theory Based Principal Component Analysis," in IEEE Transactions on Biomedical Engineering, doi: 10.1109/TBME.2022.3168592."