M. Clerc, L. Bougrain, and F. Lotte, Brain-computer Interfaces: Foundations and Methods, 2016.

J. Wolpaw and E. W. Wolpaw, Brain-computer interfaces: principles and practice, OUP USA, 2012.

F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, and B. Arnaldi, A review of classification algorithms for EEG-based brain-computer interfaces, Journal of neural engineering, 2007.
URL : https://hal.archives-ouvertes.fr/hal-01846433

A. Y. Kaplan, A. A. Fingelkurts, A. A. Fingelkurts, S. V. Borisov, and B. S. Darkhovsky, Nonstationary nature of the brain activity as revealed by EEG/MEG: methodological, practical and conceptual challenges, Signal processing, pp.2190-2212, 2005.

M. Arvaneh, C. Guan, K. K. Ang, and C. Quek, Robust EEG channel selection across sessions in brain-computer interface involving stroke patients, The 2012 International Joint Conference on Neural Networks (IJCNN), 2012.

M. Grosse-wentrup and B. Schölkopf, A review of performance variations in SMR-based Brain? Computer interfaces (BCIs), Brain-Computer Interface Research, pp.39-51, 2013.

V. Vapnik, The nature of statistical learning theory, Springer science & business media, 2013.

P. Zanini, M. Congedo, C. Jutten, S. Said, and Y. Berthoumieu, Transfer learning: a Riemannian geometry framework with applications to braincomputer interfaces, IEEE Transactions on Biomedical Engineering, pp.1-1, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01923278

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

I. Horev, F. Yger, and M. Sugiyama, Geometry-aware stationary subspace analysis, Asian Conference on Machine Learning, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01447959

F. Lotte, L. Bougrain, A. Cichocki, M. Clerc, M. Congedo et al., A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update, Journal of neural engineering, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01846433

M. Grosse-wentrup, What are the causes of performance variation in brain-computer interfacing?, International Journal of Bioelectromagnetism, pp.115-116, 2011.

F. Yger, M. Berar, and F. Lotte, Riemannian approaches in brain-computer interfaces: a review, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.25, pp.1753-1762, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01394253

M. Congedo, A. Barachant, and R. Bhatia, Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review, Brain-Computer, pp.155-174, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01570120

A. Barachant and S. Bonnet, Channel selection procedure using Riemannian distance for BCI applications, 5th International IEEE/EMBS Conference on Neural Engineering, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00602707

O. Ledoit and M. Wolf, A well-conditioned estimator for large-dimensional covariance matrices, Journal of multivariate analysis, vol.88, issue.2, pp.365-411, 2004.

Y. Renard, F. Lotte, G. Gibert, M. Congedo, E. Maby et al., OpenViBE: An Open-Source Software Platform to Design, Test and Use Brain-Computer Interfaces in Real and Virtual Environments, Presence, pp.35-53, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00477153

G. Dornhege, B. Blankertz, G. Curio, and K. R. Müller, Boosting bit rates in non-invasive EEG single-trial classifications by feature combination and multi-class paradigms, IEEE Trans. Biomed. Eng, pp.993-1002, 2004.

A. Barachant, S. Bonnet, M. Congedo, and C. Jutten, Riemannian geometry applied to bci classification, In Proc LVA-ICA, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00602700

F. Nielsen and B. Rajendra, Matrix information geometry, 2013.