References
- Barachant, A.; Bonnet, S.; Congedo, M. and Jutten, C. (2012). Multi-class Brain Computer Interface Classification by Riemannian Geometry. IEEE Transactions on Biomedical Engineering 59, 920–928.
- Congedo, M. (2018). Non-Parametric Synchronization Measures used in EEG and MEG (GIPSA-lab).
- Congedo, M.; Barachant, A. and Bhatia, R. (2017). Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review. Brain-Computer Interfaces 4, 155–174.
- Congedo, M.; Korczowski, L.; Delorme, A. and Silva, F. L. (2016). Spatio-temporal common pattern: A companion method for ERP analysis in the time domain. Journal of Neuroscience Methods 267, 74–88. Epub 2016 Apr 16.
- Ledoit, O. and Wolf, M. (2004). A well-conditioned estimator for large-dimensional covariance matrices. Journal of Multivariate Analysis 88, 365–411.
- Ledoit, O. and Wolf, M. (2020). The Power of (Non-)Linear Shrinking: A Review and Guide to Covariance Matrix Estimation. Journal of Financial Econometrics, 1–32.
- Searle, S. R. (1982). Matrix Algebra Useful for Statistics (John Wiley & Sons, New York).
- Tyler, D. E. (1987). A Distribution-Free M-Estimator of Multivariate Scatter. The Annals of Statistics 15, 234–251.
- Zhang, T. and Wiesel, A. (2016). Automatic Diagonal Loading for Tyler's Robust Covariance Estimator. In: IEEE Statistical Signal Processing Workshop (SSP); pp. 1–5.