A. Nijholt, C. S. Nam, and F. Lotte, Brain-Computer Interfaces Handbook: Technological and Theoretical Advances, 2018.

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

F. Yger, M. Berar, and F. Lotte, Riemannian approaches in braincomputer interfaces: a review, IEEE Trans Neur Syst, 2017.
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, BrainComputer Interfaces, pp.1-20, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01570120

J. Mladenovic, J. Mattout, and F. Lotte, A generic framework for adaptive EEG-based BCI training and operation, Brain-Computer Interfaces Handbook: Technological and Theoretical Advances, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01542504

P. Zanini, M. Congedo, C. Jutten, S. Said, and Y. Berthoumieu, Transfer learning: a riemannian geometry framework with applications to braincomputer interfaces, IEEE Trans Biomed Eng, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01923278

B. Reuderink, J. Farquhar, M. Poel, and A. Nijholt, A subjectindependent brain-computer interface based on smoothed, second-order baselining, Proc. IEEE EMBC. IEEE, 2011.

A. Barachant, S. Bonnet, M. Congedo, and C. Jutten, Classification of covariance matrices using a riemannian-based kernel for bci applications, Neurocomputing, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00820475

F. Yger and M. Sugiyama, Supervised logeuclidean metric learning for symmetric positive definite matrices, 2015.

A. Barachant and M. Congedo, A plug&play P300 BCI using information geometry, 2014.

H. Ramoser, J. Muller-gerking, and G. Pfurtscheller, Optimal spatial filtering of single trial eeg during imagined hand movement, IEEE Trans Rehabil Eng, 2000.

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

M. Moakher, A differential geometric approach to the geometric mean of symmetric positive-definite matrices, SIAM J Matrix Anal Appl, 2005.

B. Jeuris, R. Vandebril, and B. Vandereycken, A survey and comparison of contemporary algorithms for computing the matrix geometric mean, Electronic Transactions on Numerical Analysis, 2012.

P. Shenoy, M. Krauledat, B. Blankertz, R. Rao, and K. R. Müller, Towards adaptive classification for bci, J Neur Eng, 2006.

H. He and D. Wu, Transfer learning for brain-computer interfaces: An euclidean space data alignment approach, 2018.

M. Naeem, C. Brunner, R. Leeb, B. Graimann, and G. Pfurtscheller, Seperability of four-class motor imagery data using independent components analysis, J Neur Eng, 2006.

C. Jeunet, B. Nkaoua, S. Subramanian, M. Hachet, and F. Lotte, Predicting mental imagery-based bci performance from personality, cognitive profile and neurophysiological patterns, PloS one, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01177685

O. Ledoit and M. Wolf, A well-conditioned estimator for largedimensional covariance matrices, J Multivar Anal, 2004.

F. Lotte, Signal processing approaches to minimize or suppress calibration time in oscillatory activity-based brain-computer interfaces, Proc IEEE, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01159171

B. Blankertz, S. Lemm, M. Treder, S. Haufe, and K. Müller, Singletrial analysis and classification of erp componentsa tutorial, NeuroImage, 2011.