A. Barachant, S. Bonnet, M. Congedo, and C. , Jutten. Multiclass brain-computer interface classification by Riemannian geometry, IEEE Transactions on Biomedical Engineering, vol.59, issue.4, pp.920-928, 2012.

R. Bhatia, Positive definite matrices, 2009.
URL : https://hal.archives-ouvertes.fr/hal-02023293

H. Cho, M. Ahn, S. Ahn, M. Kwon, and S. C. Jun, EEG datasets for motor imagery brain-computer interface, GigaScience, vol.6, issue.7, pp.1-8, 2017.

M. Congedo, EEG Source Analysis . Habilitationà diriger des recherches, 2013.
URL : https://hal.archives-ouvertes.fr/tel-00880483

M. Congedo, A. Barachant, and R. Bhatia, Riemannian geometry for EEG-based braincomputer interfaces ; a primer and a review, Brain-Computer Interfaces, pp.1-20, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01570120

O. Ledoit and M. Wolf, A well-conditioned estimator for large-dimensional covariance matrices, vol.88, pp.365-411, 2004.

J. A. Roderick, D. B. Little, and . Rubin, Statistical Analysis with Missing Data, 2002.

F. Mémoli, A spectral notion of Gromov-Wasserstein distance and related methods, Applied and Computational Harmonic Analysis, vol.30, issue.3, pp.363-401, 2011.

F. Perrin, J. Pernier, O. Bertrand, and J. F. Echallier, Spherical splines for scalp potential and current density mapping. Electroencephalography and Clinical Neurophysiology, vol.72, pp.184-187, 1989.

P. L. Rodrigues, C. Jutten, and M. Congedo, Riemannian procrustes analysis : Transfer learning for braincomputer interfaces, IEEE Transactions on Biomedical Engineering, pp.1-1, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01971856