S. I. Amari, <formula formulatype="inline"><tex Notation="TeX">$\alpha$</tex> </formula>-Divergence Is Unique, Belonging to Both <formula formulatype="inline"> <tex Notation="TeX">$f$</tex></formula>-Divergence and Bregman Divergence Classes, IEEE Transactions on Information Theory, vol.55, issue.11, pp.4925-4931, 2009.
DOI : 10.1109/TIT.2009.2030485

V. Arsigny, P. Fillard, X. Pennec, and N. Ayache, Log-Euclidean metrics for fast and simple calculus on diffusion tensors, Magnetic Resonance in Medicine, vol.52, issue.2, pp.411-421, 2006.
DOI : 10.1002/mrm.20965

URL : https://hal.archives-ouvertes.fr/inria-00502678

V. Arsigny, P. Fillard, X. Pennec, and N. Ayache, Geometric Means in a Novel Vector Space Structure on Symmetric Positive???Definite Matrices, SIAM Journal on Matrix Analysis and Applications, vol.29, issue.1, pp.328-347, 2007.
DOI : 10.1137/050637996

URL : https://hal.archives-ouvertes.fr/inria-00616031

A. Barachant, A. Andreev, and M. Congedo, The Riemannian potato: an automatic and adaptive artifact detection method for online experiments using Riemannian geometry, Proceedings of TOBI Workshop IV, pp.19-20, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00781701

A. Barachant, S. Bonnet, M. Congedo, and C. Jutten, Multiclass Brain&#x2013;Computer Interface Classification by Riemannian Geometry, IEEE Transactions on Biomedical Engineering, vol.59, issue.4, pp.920-928, 2012.
DOI : 10.1109/TBME.2011.2172210

Z. Chebbi and M. Moakher, Means of Hermitian positive-definite matrices based on the log-determinant <mml:math altimg="si1.gif" overflow="scroll" xmlns:xocs="http://www.elsevier.com/xml/xocs/dtd" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.elsevier.com/xml/ja/dtd" xmlns:ja="http://www.elsevier.com/xml/ja/dtd" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:tb="http://www.elsevier.com/xml/common/table/dtd" xmlns:sb="http://www.elsevier.com/xml/common/struct-bib/dtd" xmlns:ce="http://www.elsevier.com/xml/common/dtd" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:cals="http://www.elsevier.com/xml/common/cals/dtd"><mml:mrow><mml:mi>??</mml:mi></mml:mrow></mml:math>-divergence function, Linear Algebra and its Applications, vol.436, issue.7, pp.1872-1889, 2012.
DOI : 10.1016/j.laa.2011.12.003

M. Congedo, A. Barachant, and A. Andreev, A new generation of brain-computer interface based on Riemannian geometry. arXiv preprint arXiv:1310, p.8115, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00879050

J. Fletcher and S. , Principal Geodesic Analysis on Symmetric Spaces: Statistics of Diffusion Tensors, In: Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis LNCS, vol.3117, pp.87-98, 2004.
DOI : 10.1007/978-3-540-27816-0_8

M. G. Johannes, G. Pfurtscheller, and H. Flyvbjerg, Designing optimal spatial filters for single-trial EEG classification in a movement task, Clinical Neurophysiology, vol.110, issue.5, pp.787-798, 1999.

E. K. Kalunga, S. Chevallier, O. Rabreau, and E. Monacelli, Hybrid interface: Integrating BCI in multimodal human-machine interfaces, 2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp.530-535, 2014.
DOI : 10.1109/AIM.2014.6878132

URL : https://hal.archives-ouvertes.fr/hal-01352056

E. K. Kalunga, K. Djouani, Y. Hamam, S. Chevallier, and E. Monacelli, SSVEP enhancement based on Canonical Correlation Analysis to improve BCI performances, 2013 Africon, pp.1-5, 2013.
DOI : 10.1109/AFRCON.2013.6757776

URL : https://hal.archives-ouvertes.fr/hal-01352059

F. Lotte and C. Guan, Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms, IEEE Transactions on Biomedical Engineering, vol.58, issue.2, pp.355-362, 2011.
DOI : 10.1109/TBME.2010.2082539

URL : https://hal.archives-ouvertes.fr/inria-00476820

M. Moakher, A Differential Geometric Approach to the Geometric Mean of Symmetric Positive-Definite Matrices, SIAM Journal on Matrix Analysis and Applications, vol.26, issue.3, pp.735-747, 2005.
DOI : 10.1137/S0895479803436937

E. Niedermeyer and F. L. Da-silva, Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, 2004.

F. Nielsen and R. Bhatia, Matrix Information Geometry, 2012.
DOI : 10.1007/978-3-642-30232-9

B. Rivet, A. Souloumiac, V. Attina, and G. Gibert, xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain&#x2013;Computer Interface, IEEE Transactions on Biomedical Engineering, vol.56, issue.8, pp.2035-2043, 2009.
DOI : 10.1109/TBME.2009.2012869

J. Schäfer and K. Strimmer, A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics, Statistical Applications in Genetics and Molecular Biology, vol.4, issue.1, 2005.
DOI : 10.2202/1544-6115.1175

R. Tomioka, K. Aihara, and K. R. Müller, Logistic regression for single trial EEG classification, In: NIPS. vol, vol.19, pp.1377-1384, 2007.

S. Wang and C. J. James, Enhancing evoked responses for BCI through advanced ICA techniques, pp.1-4, 2006.

F. Yger, A review of kernels on covariance matrices for BCI applications, 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), pp.1-6, 2013.
DOI : 10.1109/MLSP.2013.6661972