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.

Carrara, I. and Papadopoulo, T. (2024). Classification of BCI-EEG based on the augmented covariance matrix. IEEE Transactions on Biomedical Engineering 71, 2651–2662.

Congedo, M. (2013). EEG Source Analysis. HDR Thesis presented at the University of Grenoble.

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.; Gouy-Pailler, C. and Jutten, C. (2008). On the blind source separation of human electroencephalogram by approximate joint diagonalization of second order statistics. Clinical Neurophysiology 119, 2677–2686.

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.

Gouy-Pailler, C.; Congedo, M.; Brunner, C.; Jutten, C. and Pfurtscheller, G. (2010). Nonstationary Brain Source Separation for Multiclass Motor Imagery. IEEE Transactions on Biomedical Engineering 57, 469–478.

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.

Molgedey, L. and Schuster, H. G. (1994). Separation of a Mixture of Independent Signals Using Time Delayed Correlations. Physical Review Letters 72, 3634–3637.

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.

Wiskott, L. and Sejnowski, T. J. (2002). Slow Feature Analysis: Unsupervised Learning of Invariances. Neural Computation 14, 715–770.

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.