P. Absil, R. Mahony, and R. Sepulchre, Optimization algorithms on matrix manifolds, 2009.

M. Agueh and G. Carlier, Barycenters in the Wasserstein space, SIAM Journal on Mathematical Analysis, vol.43, issue.2, pp.904-924, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00637399

M. Aharon, M. Elad, and A. Bruckstein, K-SVD : An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation, IEEE Trans. Signal Processing, vol.54, pp.4311-4322, 2006.

B. Z. Allison and C. Neuper, Could Anyone Use a BCI ?, Brain-Computer Interfaces, pp.35-54, 2010.

, Human-Computer Interaction Series

P. Alvarez-esteban, E. Del-barrio, J. Cuesta-albertos, and C. Matrán, A fixed-point approach to barycenters in Wasserstein space, Journal of Mathematical Analysis and Applications, vol.441, pp.744-762, 2016.

C. A. Anastassiou, R. Perin, H. Markram, and C. Koch, Ephaptic coupling of cortical neurons, Nature Neuroscience, vol.14, issue.2, pp.217-223, 2011.

P. Andry, A. Blanchard, and P. Gaussier, Using the rhythm of nonverbal human-robot interaction as a signal for learning, IEEE Transactions on Autonomous Mental Development, vol.3, issue.1, pp.30-42, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00669859

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

F. Babiloni, A. Cichocki, and S. Gao, Brain-computer interfaces : towards practical implementations and potential applications. Computational Intelligence and Neuroscience, 2007.

M. Baker, 1,500 scientists lift the lid on reproducibility, Nature News, vol.533, issue.7604, p.452, 2016.

Q. Barthélemy, C. Gouy-pailler, Y. Isaac, A. Souloumiac, A. Larue et al., Multivariate Temporal Dictionary Learning for EEG, Journal of Neuroscience Methods, vol.215, pp.19-28, 2013.

Q. Barthélemy, A. Larue, A. Mayoue, D. Mercier, and J. Mars, Shift & 2D Rotation Invariant Sparse Coding for Multivariate Signals, IEEE Trans. Signal Processing, vol.60, pp.1597-1611, 2012.

Q. Barthélemy, L. Mayaud, D. Ojeda, and M. Congedo, The Riemannian potato field : a tool for online signal quality index of EEG, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.27, issue.2, pp.244-255, 2019.

M. Bekaert, C. Botte-lecoq, F. Cabestaing, and A. Rakotomamonjy, Les interfaces cerveau-machine pour la palliation du handicap moteur sévère, 2009.

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

B. Blankertz, R. Tomioka, S. Lemm, M. Kawanabe, and K. R. Muller, Optimizing Spatial filters for Robust EEG Single-Trial Analysis, Signal Processing Magazine, vol.25, issue.1, pp.41-56, 2008.

S. Blum, N. Jacobsen, M. G. Bleichner, and S. Debener, A Riemannian modification of Artifact Subspace Reconstruction for EEG artifact handling, Frontiers in human neuroscience, vol.13, p.141, 2019.

G. Borghini, P. Aricò, I. Graziani, S. Salinari, Y. Sun et al., Quantitative assessment of the training improvement in a motor-cognitive task by using EEG, ECG and EOG signals, Brain topography, vol.29, issue.1, pp.149-161, 2016.

G. Borghini, L. Astolfi, G. Vecchiato, D. Mattia, and F. Babiloni, Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness, Neuroscience & Biobehavioral Reviews, vol.44, pp.58-75, 2014.

C. Botte-lecocq, M. Bekaert, J. Vannobel, S. Leclercq, and F. Cabestaing, Considering human factors in BCI experiments : A global approach, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01114440

R. Brette, Modèles Impulsionnels de Réseaux de Neurones Biologiques, 2003.

Z. Chebbi and M. Moakher, Means of Hermitian positive-definite matrices based on the log-determinant ?-divergence function, Linear Algebra and its Applications, vol.436, issue.7, pp.1872-1889, 2012.

X. Chen, Y. Wang, S. Gao, T. Jung, and S. Gao, Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface, Journal of neural engineering, 2015.

S. Fletcher-&-joshi, Principal Geodesic Analysis on Symmetric Spaces : Statistics of Diffusion Tensors, Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis (T. 3117, pp.87-98, 2004.

J. Friedman, T. Hastie, and R. Tibshirani, The elements of statistical learning, 2001.

G. Gallego, T. Delbruck, G. Orchard, C. Bartolozzi, B. Taba et al.,

K. Daniilidis, Event-based vision : A survey, 2019.

L. George and A. Lécuyer, An overview of research on "passive" brain-computer interfaces for implicit human-computer interaction, ICABB, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00537211

P. Gergondet and A. Kheddar, SSVEP stimuli design for object-centric BCI, Brain-Computer Interfaces, vol.2, issue.1, pp.11-28, 2015.
URL : https://hal.archives-ouvertes.fr/lirmm-02155098

W. Gerstner and W. Kistler, Spiking Neuron Models : Single Neurons, Population, Plasticity, 2002.

R. Gribonval, H. Rauhut, K. Schnass, and P. Vandergheynst, Atoms of all channels, unite ! Average case analysis of multi-channel sparse recovery using greedy algorithms (rapp. tech. N o PI-1848), 2007.

M. Hallett, Transcranial magnetic stimulation and the human brain, Nature, issue.406, pp.147-150, 2000.

J. Hérault, Rétine et cortex visuel : formalisation et application au traitement des images, 1999.

V. Jayaram and A. Barachant, MOABB : trustworthy algorithm benchmarking for BCIs, Journal of neural engineering, vol.15, issue.6, p.66011, 2018.

C. Jeunet, E. Jahanpour, and F. Lotte, Why standard brain-computer interface (BCI) training protocols should be changed : an experimental study, Journal of neural engineering, vol.13, issue.3, p.36024, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01302154

C. Jeunet, F. Lotte, J. Batail, P. Philip, and J. M. Franchi, Using recent BCI literature to deepen our understanding of clinical neurofeedback : A short review, Neuroscience, vol.378, pp.225-233, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01728767

S. Joucla and B. Yvert, Modeling extracellular electrical neural stimulation : From basic understanding to MEA-based applications, Journal of Physiology, vol.106, issue.3-4, pp.146-58, 2012.

E. K. Kalunga, S. Chevallier, and Q. Barthélemy, Data augmentation in Riemannian space for Brain-Computer Interfaces, ICML workshop Stamlins, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01351990

E. K. Kalunga, S. Chevallier, Q. Barthélemy, K. Djouani, E. Monacelli et al., Online SSVEP-based BCI using Riemannian geometry, Neurocomputing, vol.191, pp.55-68, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01681976

E. K. Kalunga, S. Chevallier, O. Rabreau, and E. Monacelli, Hybrid interface : Integrating BCI in multimodal human-machine interfaces, AIM, pp.530-535, 2014.
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, AFRICON, pp.1-5, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01352059

H. Kang, Y. Nam, and S. Choi, Composite common spatial pattern for subject-to-subject transfer, Signal Processing Letters, vol.16, issue.8, pp.683-686, 2009.

K. Kreutz-delgado, J. Murray, B. Rao, K. Engan, T. Lee et al., Dictionary Learning Algorithms for Sparse Representation, Neural Comput, vol.15, pp.349-396, 2003.

E. Kristensen, A. Guerin-dugué, and B. Rivet, Comparison between Adjar and Xdawn algorithms to estimate eye-fixation related potentials distorted by overlapping, IEEE/EMBS NER, pp.976-979, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01158556

Y. Lim and M. Pálfia, Matrix power means and the Karcher mean, Journal of Functional Analysis, vol.262, issue.4, pp.1498-1514, 2012.

Z. Lin, C. Zhang, W. Wu, and X. Gao, Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs, IEEE Transactions on Biomedical Engineering, vol.53, issue.12, pp.2610-2614, 2007.

F. Lotte and C. Guan, Regularizing Common Spatial Patterns to Improve BCI Designs : Unified Theory and New Algorithms, Biomedical Engineering, vol.58, issue.2, pp.355-362, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00476820

F. Lotte and C. Jeunet, Towards improved BCI based on human learning principles, Winter Conf. on BCI, pp.1-4, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01111843

M. Luko?evi?ius, H. Jaeger, and B. Schrauwen, Reservoir computing trends, vol.26, pp.365-371, 2012.

H. Ma, Y. Hu, and H. Shi, Fault detection and identification based on the neighborhood standardized local outlier factor method, Industrial & Engineering Chemistry Research, vol.52, issue.6, pp.2389-2402, 2013.

. Bibliographie,

J. Ma and S. Perkins, Time-series novelty detection using one-class support vector machines, IJCNN (T. 3, pp.1741-1745, 2003.

A. G. Maglione, G. Vecchiato, C. A. Leone, R. Grassia, F. Mosca et al., Different perception of musical stimuli in patients with monolateral and bilateral cochlear implants. Computational and mathematical methods in medicine, 2014.

B. Mailhé, R. Gribonval, F. Bimbot, M. Lemay, P. Vandergheynst et al., Dictionary learning for the sparse modelling of atrial fibrillation in ECG signals, IEEE ICASSP, pp.465-468, 2009.

J. Mairal, F. Bach, J. Ponce, and G. Sapiro, Online Learning for Matrix Factorization and Sparse Coding, J. Mach. Learn. Res, vol.11, pp.19-60, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00408716

A. Manolova and A. Guérin-dugué, A new metric for dissimilarity data classification based on Support Vector Machines optimization, ESANN, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00977917

H. Martin, S. Chevallier, and E. Monacelli, Fast calibration of hand movement-based interface for arm exoskeleton control, European Symposium on Artificial Neural Networks (ESANN), pp.573-578, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00757886

H. Martin, S. Chevallier, and E. Monacelli, Adaptive visualisation system for construction building information models using saliency, 15th International Conference on Construction Applications of Virtual Reality, pp.317-326, 2015.

H. Martin, S. Chevallier, and E. Monacelli, Adaptive visualization for BIM experts : coping with unstructured big data in construction, 2016.

J. R. Millan, On the need for on-line learning in brain-computer interfaces, IEEE IJCNN, pp.2877-2882, 2004.

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.

M. Moakher and P. G. Batchelor, Symmetric Positive-Definite Matrices : From Geometry to Applications and Visualization, Visualization and Processing of Tensor Fields, vol.17, pp.285-298, 2006.

G. G. Molina, T. Tsoneva, and A. Nijholt, Emotional brain-computer interfaces, ICACII, pp.1-9, 2009.

G. Monaci, P. Jost, P. Vandergheynst, B. Mailhé, S. Lesage et al., Learning multimodal dictionaries, IEEE Trans. Image Processing, vol.16, pp.2272-2283, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00544772

G. Bibliographie-monaci, P. Vandergheynst, and F. Sommer, Learning Bimodal Structure in Audio-Visual Data, IEEE Trans. Neural Networks, vol.20, pp.1898-1910, 2009.

M. Nakanishi, Y. Wang, Y. Wang, Y. Mitsukura, and T. Jung, Highspeed brain speller using steady-state visual evoked potentials. International journal of neural systems, vol.24, p.1450019, 2014.

N. Naseer and K. Hong, fNIRS-based brain-computer interfaces : a review, Frontiers in human neuroscience, vol.9, p.3, 2015.

Y. Ollivier, Riemannian metrics for neural networks II : recurrent networks and learning symbolic data sequences, Information and Inference, vol.4, issue.2, pp.154-193, 2015.
URL : https://hal.archives-ouvertes.fr/hal-00857980

B. Olshausen and D. Field, Sparse coding with an overcomplete basis set : a strategy employed by V1 ? Vision Research, vol.37, pp.3311-3325, 1997.

S. Ozen, A. Sirota, M. A. Belluscio, C. A. Anastassiou, E. Stark et al., Transcranial electric stimulation entrains cortical neuronal populations in rats, Journal of Neuroscience, vol.30, issue.34, pp.11476-11485, 2010.

L. Perez and J. Wang, The effectiveness of data augmentation in image classification using deep learning, 2017.

L. Perrinet, Sparse Spike Coding : applications of Neuroscience to the processing of natural images, SPIE Photonics Europe, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00276638

L. U. Perrinet, An Adaptive Homeostatic Algorithm for the Unsupervised Learning of Visual Features, vol.Vision, p.47, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02296037

L. Perrinet, M. Samuelides, and S. Thorpe, Coding static natural images using spiking event times : do neurons cooperate ?, IEEE Transactions on neural networks, vol.15, issue.5, pp.1164-1175, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00110803

G. Peyré and M. Cuturi, Computational optimal transport. Foundations and Trends in Machine Learning, vol.11, pp.355-607, 2019.

T. H. Phuoc, A. Guérin-dugué, and N. Guyader, A computational saliency model integrating saccade programming, International Conference on Bio-inspired Systems and Signal Processing, pp.57-64, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00370472

T. Ho-phuoc, N. Guyader, F. Landragin, and A. Guérin-dugué, When viewing natural scenes, do abnormal colors impact on spatial or temporal parameters of eye movements, Journal of Vision, vol.12, issue.2, pp.4-4, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00680776

A. Rakotomamonjy, Surveying and comparing simultaneous sparse approximation (or group-lasso) algorithms. Signal Process, vol.91, pp.1505-1526, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00328185

Z. Tiganj, S. Chevallier, and E. Monacelli, Influence of extracellular oscillations on neural communication : A computational perspective, Frontiers in Computational Neuroscience, vol.8, issue.9, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01214044

S. Chevallier, Q. Barthélémy, and J. Atif, Subspace metrics for multivariate dictionaries and application to EEG, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2014.
URL : https://hal.archives-ouvertes.fr/hal-01352047

K. Emmanuel, S. Kalunga, Q. Chevallier, K. Barthélémy, Y. Djouani et al., Online SSVEP-based BCI using Riemannian Geometry, pp.55-68, 2016.

Y. Yang, S. Chevallier, J. Wiart, and I. Bloch, Subjectspecific time-frequency selection for multi-class motor imagery-based BCIs using few Laplacian EEG channels, Biomedical Signal Processing and Control, vol.38, pp.302-311, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02287605

E. Kalunga, S. Chevallier, and Q. Barthélemy, Transfer learning for SSVEP-based BCI using Riemannian similarities between users, EUSIPCO, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01911092

E. A. Allen, B. N. Pasley, T. Duong, and R. D. Freeman, Transcranial magnetic stimulation elicits coupled neural and hemodynamic consequences, Science, vol.317, pp.1918-1921, 2007.

C. A. Anastassiou, S. M. Montgomery, M. Barahona, G. Buzsáki, and C. Koch, The effect of spatially inhomogeneous extracellular electric fields on neurons, J. Neurosci, vol.30, pp.1925-1936, 2010.

C. A. Anastassiou, R. Perin, H. Markram, and C. Koch, Ephaptic coupling of cortical neurons, Nat. Neurosci, vol.14, pp.217-223, 2011.

A. T. Barker, R. Jalinous, and I. L. Freeston, Non-invasive magnetic stimulation of human motor cortex, Lancet, vol.1, issue.85, pp.92413-92417, 1985.

A. L. Benabid, S. Chabardes, J. Mitrofanis, and P. Pollak, Deep brain stimulation of the subthalamic nucleus for the treatment of Parkinson's disease, Lancet Neurol, vol.8, pp.67-81, 2009.

R. Brette and A. Destexhe, Handbook of Neural Activity Measurement, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00739666

G. Buzsáki, Hippocampal sharp waves: their origin and significance, Brain Res, vol.398, issue.86, pp.91483-91489, 1986.

G. Buzsáki, C. A. Anastassiou, and C. Koch, The origin of extracellular fields and currents -EEG, ECoG, LFP and spikes, Nat. Rev. Neurosci, vol.13, pp.407-420, 2012.

G. Buzsáki and A. Draguhn, Neuronal oscillations in cortical networks, Science, vol.304, pp.1926-1929, 2004.

P. Capotosto, C. Babiloni, G. L. Romani, and M. Corbetta, Frontoparietal cortex controls spatial attention through modulation of anticipatory alpha rhythms, J. Neurosci, vol.29, pp.5863-5872, 2009.

N. T. Carnevale and M. L. Hines, The NEURON Book, 2006.

J. K. Deans, A. D. Powell, and J. G. Jefferys, Sensitivity of coherent oscillations in rat hippocampus to AC electric fields, J. Physiol, vol.583, pp.555-565, 2007.

G. Deco and A. Thiele, Attention: oscillations and neuropharmacology, Eur. J. Neurosci, vol.30, pp.347-354, 2009.

J. S. Ebersole, M. , and J. , The electroencephalogram (EEG): a measure of neural synchrony, Epilepsy as a Dynamic Disease, pp.51-68, 2003.

P. Fries, D. Nikolic, and W. Singer, The gamma cycle, Trends Neurosci, vol.30, pp.309-316, 2007.

F. Fröhlich and D. A. Mccormick, Endogenous electric fields may guide neocortical network activity, Neuron, vol.67, pp.129-143, 2010.

M. Hallett, Transcranial magnetic stimulation and the human brain, Nature, vol.406, pp.147-150, 2000.

P. Hemond, D. Epstein, A. Boley, M. Migliore, G. A. Ascoli et al., Distinct classes of pyramidal cells exhibit mutually exclusive firing patterns in hippocampal area CA3b, Hippocampus, vol.18, pp.411-424, 2008.

M. L. Hines and N. T. Carnevale, The NEURON simulation environment, Neural Comput, vol.9, pp.1179-1209, 1997.

E. M. Izhikevich, Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting, 2006.

S. Joucla, Y. , and B. , Modeling extracellular electrical neural stimulation: from basic understanding to mea-based applications, J. Physiol, vol.106, pp.146-158, 2012.

M. J. Kahana, D. Seelig, and J. R. Madsen, Theta returns, Curr. Opin. Neurobiol, vol.11, pp.739-744, 2001.

C. Kayser, M. A. Montemurro, N. K. Logothetis, and S. Panzeri, Spikephase coding boosts and stabilizes information carried by spatial and temporal spike patterns, Neuron, vol.61, pp.597-608, 2009.

S. Kim, S. J. Guzman, H. , H. , J. et al., Active dendrites support efficient initiation of dendritic spikes in hippocampal ca3 pyramidal neurons, Nat. Neurosci, vol.15, pp.600-606, 2012.

R. Kirov, C. Weiss, H. R. Siebner, J. Born, M. et al., Slow oscillation electrical brain stimulation during waking promotes EEG theta activity and memory encoding, Proc. Natl. Acad. Sci. U.S.A, vol.106, pp.15460-15465, 2009.

K. Koepsell, X. Wang, J. A. Hirsch, and F. T. Sommer, Exploring the function of neural oscillations in early sensory systems, Front. Neurosci, vol.4, p.53, 2010.

J. Lisman and G. Buzsáki, A neural coding scheme formed by the combined function of gamma and theta oscillations, Schizophr. Bull, vol.34, pp.974-980, 2008.

Z. F. Mainen and T. J. Sejnowski, Modeling active dendritic processes in pyramidal neurons, Methods in Neuronal Modeling, pp.170-209, 1998.

H. Markram, The blue brain project, Nat. Rev. Neurosci, vol.9, pp.153-160, 2006.

M. Migliore, M. Ferrante, and G. A. Ascoli, Signal propagation in oblique dendrites of ca1 pyramidal cells, J. Neurophysiol, vol.94, pp.4145-4155, 2005.

M. A. Montemurro, M. J. Rasch, Y. Murayama, N. K. Logothetis, and S. Panzeri, Phase-of-firing coding of natural visual stimuli in primary visual cortex, Curr. Biol, vol.18, pp.375-380, 2008.

E. Nyhus and T. Curran, Functional role of gamma and theta oscillations in episodic memory, Neurosci. Biobehav. Rev, vol.34, pp.1023-1035, 2010.

D. Osipova, A. Takashima, R. Oostenveld, G. Fernandez, E. Maris et al., Theta and gamma oscillations predict encoding and retrieval of declarative memory, J. Neurosci, vol.26, pp.7523-7531, 2006.

S. Ozen, A. Sirota, M. A. Belluscio, C. A. Anastassiou, E. Stark et al., Transcranial electric stimulation entrains cortical neuronal populations in rats, J. Neurosci, vol.30, pp.11476-11485, 2010.

L. C. Parra and M. Bikson, Model of the effect of extracellular fields on spike time coherence, Conf. Proc. IEEE Eng. Med. Biol. Soc, vol.6, pp.4584-4587, 2004.

J. E. Peelle and M. H. Davis, Neural oscillations carry speech rhythm through to comprehension, Front. Psychol, vol.3, p.320, 2012.

G. Pyapali, A. Sik, M. Penttonen, G. Buzsaki, and D. Turner, Dendritic properties of hippocampal ca1 pyramidal neurons in the rat: intracellular staining in vivo and in vitro, J. Comp. Neurol, vol.391, pp.335-352, 1998.

T. Radman, Y. Su, J. H. An, L. C. Parra, and M. Bikson, Spike timing amplifies the effect of electric fields on neurons: implications for endogenous field effects, J. Neurosci, vol.27, pp.3030-3036, 2007.

D. Reato, A. Rahman, M. Bikson, and L. C. Parra, Low-intensity electrical stimulation affects network dynamics by modulating population rate and spike timing, J. Neurosci, vol.30, pp.15067-15079, 2010.

A. V. Samsonovich and G. A. Ascoli, Statistical morphological analysis of hippocampal principal neurons indicates cell-specific repulsion of dendrites from their own cell, J. Neurosci. Res, vol.71, pp.173-187, 2003.

B. Schack, N. Vath, H. Petsche, H. G. Geissler, and E. Möller, Phasecoupling of theta-gamma EEG rhythms during short-term memory processing, Int. J. Psychophysiol, vol.44, pp.143-163, 2002.

C. E. Schroeder and P. Lakatos, Low-frequency neuronal oscillations as instruments of sensory selection, Trends Neurosci, vol.32, pp.9-18, 2009.

P. B. Sederberg, M. J. Kahana, M. W. Howard, E. J. Donner, and J. R. Madsen, Theta and gamma oscillations during encoding predict subsequent recall, J. Neurosci, vol.23, pp.10809-10814, 2003.

N. Spruston, Pyramidal neurons: dendritic structure and synaptic integration, Nat. Rev. Neurosci, vol.9, pp.206-221, 2008.

D. Sullivan, J. Csicsvari, K. Mizuseki, S. Montgomery, K. Diba et al., Relationships between hippocampal sharp waves, ripples, and fast gamma oscillation: influence of dentate and entorhinal cortical activity, J. Neurosci, vol.31, pp.8605-8616, 2011.

G. Thut, A. Nietzel, S. A. Brandt, and A. Leone, alpha-band electroencephalographic activity over occipital cortex indexes visuospatial attention bias and predicts visual target detection, J. Neurosci, vol.26, pp.9494-9502, 2006.

P. Uhlhaas, F. Roux, E. Rodriguez, A. Rotarska-jagiela, and W. Singer, Neural synchrony and the development of cortical networks, Trends Cogn. Sci, vol.14, pp.72-80, 2010.

V. Walsh and A. Cowey, Transcranial magnetic stimulation and cognitive neuroscience, Nat. Rev. Neurosci, vol.1, pp.73-79, 2000.

X. J. Wang, Neurophysiological and computational principles of cortical rhythms in cognition, Physiol. Rev, vol.90, pp.1195-1268, 2008.

L. M. Ward, Synchronous neural oscillations and cognitive processes, Trends Cogn. Sci, vol.7, pp.553-559, 2003.

S. A. Weiss and D. S. Faber, Field effects in the CNS play functional roles, Front. Neural Circuits, vol.4, p.15, 2010.

C. Wilson, Up and down states, vol.3, p.1410, 2008.

, Conflict of Interest Statement: The authors declare that the research was con

K. Engan, S. O. Aase, and J. H. Husøy, Multi-frame compression: theory and design, Signal Processing, vol.80, pp.2121-2140, 2000.

R. Grosse, R. Raina, H. Kwong, and A. Y. Ng, Shift-invariant sparse coding for audio classification, Proc. Conf. on Uncertainty in Artificial Intelligence (UAI), pp.149-158, 2007.

J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, Non-local sparse models for image restoration, IEEE ICCV, pp.2272-2279, 2009.
URL : https://hal.archives-ouvertes.fr/hal-02414291

M. Aharon, M. Elad, and A. M. Bruckstein, K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation, IEEE Trans. Signal Processing, vol.54, pp.4311-4322, 2006.

A. Aldroubi, Portraits of Frames, Proceedings of the American Mathematical Society, vol.123, issue.6, pp.1661-1668, 1995.

D. Vainsencher, S. Mannor, and A. M. Bruckstein, The sample complexity of dictionary learning, Journal of Machine Learning Research, vol.12, pp.3259-3281, 2011.

E. M. Hammer, S. Halder, B. Blankertz, C. Sannelli, T. Dickhaus et al., Psychological predictors of SMR-BCI performance, Biological Psychology, vol.89, issue.1, pp.80-86, 2012.

C. Vidaurre and B. Blankertz, Towards a cure for BCI illiteracy, Brain Topography, vol.23, issue.2, pp.194-198, 2010.

K. Engan, K. Skretting, and J. H. Husøy, Family of iterative LS-based dictionary learning algorithms, ILS-DLA, for sparse signal representation, Digital Signal Processing, vol.17, pp.32-49, 2007.

J. Mairal, F. Bach, J. Ponce, and G. Sapiro, Online learning for matrix factorization and sparse coding, Journal of Machine Learning Research, vol.11, pp.19-60, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00408716

J. A. Tropp, Algorithms for simultaneous sparse approximation; Part II: Convex relaxation, Signal Processing, vol.86, pp.589-602, 2006.

R. Gribonval, H. Rauhut, K. Schnass, and P. Vandergheynst, Atoms of all channels, unite! Average case analysis of multichannel sparse recovery using greedy algorithms, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00146660

A. Rakotomamonjy, Surveying and comparing simultaneous sparse approximation (or group-lasso) algorithms, Signal Processing, vol.91, pp.1505-1526, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00328185

Q. Barthélemy, C. Gouy-pailler, Y. Isaac, A. Souloumiac, A. Larue et al., Multivariate temporal dictionary learning for EEG, Journal of Neuroscience Methods, vol.215, pp.19-28, 2013.

Q. Barthélemy, A. Larue, A. Mayoue, D. Mercier, and J. I. Mars, Shift & 2D rotation invariant sparse coding for multivariate signals, IEEE Trans. Signal Processing, vol.60, pp.1597-1611, 2012.

G. H. Golub and H. Zha, The canonical correlations of matrix pairs and their numerical computation, of The IMA Volumes in Mathematics and its Applications, vol.69, pp.27-49, 1995.

G. H. Golub and C. F. Van-loan, Matrix Computations, 1996.

J. H. Conway, R. H. Hardin, and N. J. Sloane, Packing lines, planes, etc.: Packings in Grassmannian spaces, Experimental Mathematics, vol.5, issue.2, pp.139-159, 1996.

I. S. Dhillon, R. W. Heath, T. Strohmer, and J. A. Tropp, Constructing Packings in Grassmannian Manifolds via Alternating Projection, vol.17, pp.9-35, 2008.

A. Edelman, T. A. Arias, and S. T. Smith, The Geometry of Algorithms with Orthogonality Constraints, SIAM J. Matrix Anal. Appl, vol.20, issue.2, pp.303-353, 1999.

J. W. Milnor and J. D. Stasheff, Characteristic Classes. (AM-76), vol.76, 1974.

C. Villani, Grundlehren der mathematischen Wissenschaften, vol.338, 2009.

M. Tangermann, K. Müller, A. Aertsen, N. Birbaumer, C. Braun et al., Review of the BCI Competition IV, vol.6, issue.55, 2012.

M. and A. , Overcomplete Dictionaries for Sparse Representation of Signals, 2006.

Y. Rubner, C. Tomasi, and L. J. Guibas, A metric for distributions with applications to image databases, International Conference on Computer Vision (ICCV), pp.59-66, 1998.

O. Pele and M. Werman, Fast and robust earth mover's distances, International Conference on Computer Vision (ICCV, pp.460-467, 2009.

K. K. Ang, Z. Y. Chin, C. Wang, C. Guan, and H. Zhang, Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b, Frontiers in Neuroscience, vol.6, pp.1-9, 2012.

B. J. Frey and D. Dueck, Clustering by passing messages between data points, Science, vol.315, pp.972-976, 2007.

A. Strehl and J. Ghosh, Cluster ensembles -a knowledge reuse framework for combining multiple partitions, Journal of Machine Learning Research, vol.3, pp.583-617, 2003.

J. R. Wolpaw, N. Birbaumer, D. J. Mcfarland, G. Pfurtscheller, and T. M. Vaughan, Brain-computer interfaces for communication and control, vol.113, issue.6, pp.767-791, 2002.

J. J. Vidal, Toward direct brain-computer communication, Annu. Rev. Biophys. Bioeng, vol.2, issue.1, pp.157-180, 1973.

J. D. Bayliss and D. H. Ballard, Single trial P3 epoch recognition in a virtual environment, Neurocomputing, vol.32, pp.637-642, 2000.

W. Tu and S. Sun, A subject transfer framework for EEG classification, Neurocomputing, vol.82, pp.109-116, 2012.

E. Niedermeyer, F. Lopes-da, and S. , Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, 2004.

B. Blankertz, K. R. Müller, G. Curio, T. M. Vaughan, G. Schalk et al., The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials, IEEE Trans. Biomed. Eng, vol.51, issue.6, pp.1044-1051, 2004.

B. Blankertz, K. R. Muller, D. J. Krusienski, G. Schalk, J. R. Wolpaw et al., The BCI competition III: validating alternative approaches to actual BCI problems, IEEE Trans. Neural Syst. Rehabil. Eng, vol.14, issue.2, pp.153-159, 2006.

M. Tangermann, K. Müller, A. Aertsen, N. Birbaumer, C. Braun et al., Review of the BCI Competition IV, vol.6, issue.55

T. Dickhaus, C. Sannelli, K. Müller, G. Curio, and B. Blankertz, Predicting BCI performance to study BCI illiteracy, BMC Neurosci, vol.10, issue.1, pp.1-2, 2009.

B. Z. Allison and C. Neuper, Could anyone use a BCI?, Brain-Computer Interfaces, pp.35-54, 2010.

C. Vidaurre and B. Blankertz, Towards a cure for BCI illiteracy, Brain Topogr, vol.23, issue.2, pp.194-198, 2010.

B. Obermaier, C. Guger, C. Neuper, and G. Pfurtscheller, Hidden Markov models for online classification of single trial EEG data, Pattern Recognit. Lett, vol.22, issue.12, pp.1299-1309, 2001.

A. Lenhardt, M. Kaper, and H. Ritter, An adaptive P300-based online brain computer interface, IEEE Trans. Neural Syst. Rehabil. Eng, vol.16, issue.2, pp.121-130, 2008.

L. F. Nicolas-alonso, R. Corralejo, J. Gomez-pilar, D. Álvarez, and R. Hornero, Adaptive semi-supervised classification to reduce intersession non-stationarity in multiclass motor imagery-based brain-computer interfaces, Neurocomputing, 2016.

E. Kalunga, K. Djouani, Y. Hamam, S. Chevallier, and E. Monacelli, SSVEP enhancement based on Canonical Correlation Analysis to improve bci performances, pp.1-5, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01352059

H. Lu, H. Eng, C. Guan, K. Plataniotis, and A. Venetsanopoulos, Regularized common spatial pattern with aggregation for EEG classification in smallsample setting, IEEE Trans. Biomed. Eng, vol.57, issue.12, pp.2936-2946, 2010.

B. Blankertz, R. Tomioka, S. Lemm, M. Kawanabe, and K. R. Muller, Optimizing spatial filters for robust EEG single-trial analysis, IEEE Signal Process. Mag, vol.25, issue.1, pp.41-56, 2008.

F. Lotte and C. Guan, Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms, IEEE Trans. Biomed. Eng, vol.58, issue.2, pp.355-362, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00476820

Y. Yang, S. Chevallier, J. Wiart, and I. Bloch, Automatic selection of the number of spatial filters for motor-imagery BCI, European Symposium on Artificial Neural Networks (ESANN), pp.109-114, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00737242

Z. Lin, C. Zhang, W. Wu, and X. Gao, Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs, IEEE Trans. Biomed. Eng, vol.53, issue.12, pp.2610-2614, 2006.

P. Absil, R. Mahony, and R. Sepulchre, Optimization Algorithms on Matrix Manifolds, 2009.

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

A. Barachant, S. Bonnet, M. Congedo, and C. Jutten, Multiclass brain-computer interface classification by Riemannian geometry, IEEE Trans. Biomed. Eng, vol.59, issue.4, pp.920-928, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00681328

A. Barachant, S. Bonnet, M. Congedo, and C. Jutten, Classification of covariance matrices using a Riemannian-based kernel for BCI applications, Neurocomputing, vol.112, pp.172-178, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00820475

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

E. K. Kalunga, S. Chevallier, O. Rabreau, and E. Monacelli, Hybrid interface: integrating BCI in multimodal human-machine interfaces, IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp.530-535, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01352056

X. Gao, D. Xu, M. Cheng, and S. Gao, A BCI-based environmental controller for the motion-disabled, IEEE Trans. Neural Syst. Rehabil. Eng, vol.11, issue.2, pp.137-140, 2003.

G. Edlinger, C. Holzner, and C. Guger, Human-Computer Interaction. Interaction Techniques and Environments, Lecture Notes in Computer Science, vol.6762, pp.417-426, 2011.

R. C. Panicker, S. Puthusserypady, and Y. Sun, Adaptation in P300 brain-computer interfaces: a two-classifier cotraining approach, IEEE Trans. Biomed. Eng, vol.57, issue.12, pp.2927-2935, 2010.

F. Schettini, F. Aloise, P. Aricò, S. Salinari, D. Mattia et al., Self-calibration algorithm in an asynchronous P300-based brain-computer interface, J. Neural Eng, vol.11, issue.3, p.35004, 2014.

H. Verschore, P. Kindermans, D. Verstraeten, and B. Schrauwen, Dynamic stopping improves the speed and accuracy of a P300 speller, Artificial Neural Networks and Machine Learning-ICANN 2012, pp.661-668, 2012.

D. Regan, Comparison of transient and steady-state methods, Ann. N.Y. Acad. Sci, vol.388, issue.1, pp.45-71, 1982.

T. Takahashi and K. H. Chiappa, Activation methods, Electroencephalography. in: Basic Principles, Clinical Applications, and Related Fields, pp.241-262, 2004.

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

J. Wolpaw, N. Birbaumer, D. J. Mcfarland, G. Pfurtscheller, and T. M. Vaughan, Braincomputer interfaces for communication and control, vol.113, issue.6, pp.767-791, 2002.

S. T. Morgan, J. C. Hansen, and S. A. Hillyard, Selective attention to stimulus location modulates the steady-state visual evoked potential, Proc. Natl. Acad. Sci. USA, vol.93, issue.10, pp.4770-4774, 1996.

M. M. Müller, S. Andersen, N. J. Trujillo, P. Valdés-sosa, P. Malinowski et al., Feature-selective attention enhances color signals in early visual areas of the human brain, Proc. Natl. Acad. Sci, vol.103, pp.14250-14254, 2006.

B. Allison, T. Lüth, D. Valbuena, A. Teymourian, I. Volosyak et al., BCI demographics: how many (and what kinds of) people can use an SSVEP BCI?, IEEE Trans. Neural Syst. Rehabil. Eng, vol.18, issue.2, pp.107-116, 2010.

D. Zhu, J. Bieger, G. G. Molina, and R. M. Aarts, A survey of stimulation methods used in SSVEP-based BCIs, Intell. Neurosci, 2010.

C. S. Herrmann, Human EEG responses to 1100 hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena, Exp, Brain Res, vol.137, pp.346-353, 2001.

M. A. Pastor, J. Artieda, J. Arbizu, M. Valencia, and J. C. Masdeu, Human cerebral activation during steady-state visual-evoked responses, J. Neurosci, vol.23, issue.37, pp.11621-11627, 2003.

R. S. Fisher, G. Harding, G. Erba, G. L. Barkley, and A. Wilkins, Photic-and patterninduced seizures: a review for the epilepsy foundation of america working group, Epilepsia, vol.46, issue.9, pp.1426-1441, 2005.

J. Pan, X. Gao, F. Duan, Z. Yan, and S. Gao, Enhancing the classification accuracy of steady-state visual evoked potential-based brain-computer interfaces using phase constrained canonical correlation analysis, J. Neural Eng, vol.8, issue.3, 2011.

M. Nakanishi, Y. Wang, Y. Wang, Y. Mitsukura, and T. Jung, A high-speed brain speller using steady-state visual evoked potentials, Int. J. Neural Syst, vol.24, issue.06, p.1450019, 2014.

M. Spüler, W. Rosenstiel, and M. Bogdan, Online adaptation of a c-VEP braincomputer interface (BCI) based on error-related potentials and unsupervised learning, PLoS ONE, vol.7, issue.12, 2012.

G. Bin, X. Gao, Y. Wang, Y. Li, B. Hong et al., A high-speed BCI based on code modulation VEP, J. Neural Eng, vol.8, issue.2, 2011.

H. Cecotti, A self-paced and calibration-less SSVEP-based brain-computer interface speller, IEEE Trans. Neural Syst. Rehabil. Eng, vol.18, issue.2, pp.127-133, 2010.

S. Parini, L. Maggi, A. C. Turconi, and G. Andreoni, A robust and self-paced BCI system based on a four class SSVEP paradigm: algorithms and protocols for a high-transfer-rate direct brain communication, Intell. Neurosci, 2009.

F. Yger, IEEE International Workshop on Machine Learning for Signal Processing, pp.1-6, 2013.

S. Jayasumana, R. Hartley, M. Salzmann, H. Li, and M. Harandi, Kernel methods on the Riemannian manifold of symmetric positive definite matrices, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.73-80, 2013.

Y. Xie, J. Ho, and B. Vemuri, On a nonlinear generalization of sparse coding and dictionary learning, International Conference on Machine Learning (ICML), p.1480, 2013.

A. Goh and R. Vidal, Unsupervised Riemannian clustering of probability density functions, Machine Learning and Knowledge Discovery in Databases, pp.377-392, 2008.

A. Goh and R. Vidal, Clustering and dimensionality reduction on Riemannian manifolds, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1-7, 2008.

X. Pennec, P. Fillard, and N. Ayache, A Riemannian framework for tensor computing, Int. J. Comput. Vis, vol.66, issue.1, pp.41-66, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00070743

A. Barachant, S. Bonnet, M. Congedo, and C. Jutten, Riemannian geometry applied to BCI classification, Latent Variable Analysis and Signal Separation, pp.629-636, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00602700

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

S. Amari, ?-divergence is unique, belonging to both f-divergence and Bregman divergence classes, IEEE Trans. Inf. Theory, vol.55, issue.11, pp.4925-4931, 2009.

W. Samek, D. Blythe, K. Müller, and M. Kawanabe, Robust spatial filtering with beta divergence, Advances in NeuralInformation Processing Systems (NIPS), pp.1007-1015, 2013.

W. Samek and K. Muller, Information geometry meets BCI spatial filtering using divergences, International Winter Workshop on Brain-Computer Interface, pp.1-4, 2014.

A. Barachant and S. Bonnet, Channel selection procedure using Riemannian distance for BCI applications, International IEEE/EMBS Conference on Neural Engineering, pp.348-351, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00602707

A. Barachant, S. Bonnet, M. Congedo, and C. Jutten, Common spatial pattern revisited by Riemannian geometry, IEEE International Workshop on Multimedia Signal Processing (MMSP), pp.472-476, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00602686

Y. Li, K. M. Wong, and H. D. Bruin, EEG signal classification based on a Riemannian distance measure, Science and Technology for Humanity, 2009.

, IEEE Toronto International Conference, pp.268-273, 2009.

Y. Li, K. Wong, and H. D. Bruin, Electroencephalogram signals classification for sleepstate decision: a Riemannian geometry approach, IET Signal Process, vol.6, issue.4, pp.288-299, 2012.

J. Jost, Riemannian Geometry and Geometric Analysis, vol.62011, 2011.

M. Moakher, A differential geometric approach to the geometric mean of symmetric positive-definite matrices, SIAM J. Matrix Anal. Appl, vol.26, issue.3, pp.735-747, 2005.

P. T. Fletcher, C. Lu, S. M. Pizer, and S. Joshi, Principal geodesic analysis for the study of nonlinear statistics of shape, IEEE Trans. Med. Imag, vol.23, issue.8, pp.995-1005, 2004.

K. Fukunaga, Introduction to Statistical Pattern Recognition, 1990.

O. Ledoit and M. Wolf, A well-conditioned estimator for large-dimensional covariance matrices, J. Multivar. Anal, vol.88, issue.2, pp.365-411, 2004.

B. Blankertz, S. Lemm, M. Treder, S. Haufe, and K. Müller, Single-trial analysis and classification of ERP components: a tutorial, NeuroImage, vol.56, issue.2, pp.814-825, 2011.

J. Schäfer and K. Strimmer, A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics, Stat. Appl. Genet. Mol. Biol, vol.4, issue.1

F. Pascal, P. Forster, J. P. Ovarlez, and P. , Theoretical analysis of an improved covariance matrix estimator in non-gaussian noise, IEEE International Conference on Acoustics, Speech, and Signal Processing, vol.4, 2005.
URL : https://hal.archives-ouvertes.fr/halshs-00158387

M. Congedo, EEG source analysis, Habilitation á diriger des recherches, Université de Grenoble, 2013.

M. J. Pencina, R. B. Agostino, and R. S. Vasan, Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond, Stat. Med, vol.27, issue.2, pp.157-172, 2008.

Y. Kimura, T. Tanaka, H. Higashi, and N. Morikawa, SSVEP-based brain-computer interfaces using FSK-modulated visual stimuli, IEEE Trans. Biomed. Eng, vol.60, issue.10, pp.2831-2838, 2013.

F. Vialatte, M. Maurice, J. Dauwels, and A. Cichocki, Steady-state visually evoked potentials: focus on essential paradigms and future perspectives, Prog. Neurobiol, vol.90, issue.4, pp.418-438, 2010.

H. Bakardjian, T. Tanaka, and A. Cichocki, Optimization of SSVEP brain responses with application to eight-command brain-computer interface, Neurosci. Lett, vol.469, issue.1, pp.34-38, 2010.

A. Vallabhaneni, T. Wang, and B. He, Brain-computer interface, pp.85-121, 2005.

E. A. Curran and M. J. Stokes, Learning to control brain activity: a review of the production and control of EEG components for driving brain-computer interface (BCI) systems, Brain Cogn, vol.51, pp.326-336, 2003.

L. A. Farwell and E. Donchin, Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials, Electroencephalogr. Clin. Neurophysiol, vol.70, pp.510-523, 1988.

J. Jin, B. Z. Allison, E. W. Sellers, C. Brunner, P. Horki et al., An adaptive p300-based control system, J. Neural Eng, vol.8, p.36006, 2011.

J. J. Vidal, Toward direct brain-computer communication, Annu. Rev. Biophys. Bioeng, vol.2, pp.157-180, 1973.

X. Chen, Y. Wang, M. Nakanishi, X. Gao, T. Jung et al., High-speed spelling with a noninvasive brain-computer interface, Proc. Natl. Acad. Sci. U. S. A, vol.112, pp.6058-6067, 2015.

Z. Qiu, B. Z. Allison, J. Jin, Y. Zhang, X. Wang et al., Optimized motor imagery paradigm based on imagining Chinese characters writing movement, IEEE Trans. Neural Syst. Rehabil. Eng, 2017.

G. Pfurtscheller, B. Z. Allison, C. Brunner, G. Bauernfeind, T. Solis-escalante et al., The hybrid BCI, Front. Neurosci, vol.2, pp.1-12, 2010.

M. Wang, I. Daly, B. Z. Allison, J. Jin, Y. Zhang et al., A new hybrid BCI paradigm based on p300 and SSVEP, J. Neurosci. Methods, vol.244, pp.16-25, 2015.

G. Pfurtscheller, C. Brunner, A. Schlögl, F. H. Lopes-da, and S. , Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks, Neuroimage, vol.31, pp.153-159, 2006.

S. Silvoni, A. Ramos-murguialday, M. Cavinato, C. Volpato, G. Cisotto et al., Brain-computer interface in stroke: a review of progress, Clin. EEG Neurosci, vol.42, pp.245-252, 2011.

R. Swaminathan and S. Prasad, Brain computer interface used in health care technologies, Next Generation DNA Led Technologies, pp.49-58, 2016.

A. Kübler, F. Nijboer, J. Mellinger, T. M. Vaughan, H. Pawelzik et al., Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface, Neurology, vol.64, pp.1775-1777, 2005.

R. Scherer, A. Schlögl, F. Lee, H. Bischof, J. Jan?a et al., The self-paced Graz brain-computer interface: methods and applications, Comput. Intell. Neurosci, p.79826, 2007.

Y. Yang, J. Wiart, and I. Bloch, Towards next generation human-computer interaction-brain-computer interfaces: applications and challenges, 1st International Symposium of Chinese CHI (Chinese CHI 2013, pp.1-2, 2013.
URL : https://hal.archives-ouvertes.fr/hal-02286495

, D virtual reality environments

S. Liang, K. Choi, J. Qin, W. Pang, Q. Wang et al., Improving the discrimination of hand motor imagery via virtual reality based visual guidance, Comput. Methods Programs Biomed, vol.132, pp.63-74, 2016.

B. Blankertz, R. Tomioka, S. Lemm, M. Kawanabe, and K. R. Müller, Optimizing spatial filters for robust EEG single-trial analysis, IEEE Signal Process. Mag, vol.25, pp.41-56, 2008.

Y. Yang, S. Chevallier, J. Wiart, and I. Bloch, Automatic selection of the number of spatial filters for motor-imagery BCI, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp.109-114, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00737242

S. Wang and C. J. James, Extracting rhythmic brain activity for brain-computer interfacing through constrained independent component analysis, Comput. Intell. Neurosci, p.41468, 2007.

Y. Yang, I. Bloch, S. Chevallier, and J. Wiart, Subject-specific channel selection using time information for motor imagery brain-computer interfaces, Cogn. Comput, vol.8, pp.505-518, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01351620

Y. Yang, O. Kyrgyzov, J. Wiart, and I. Bloch, Subject-specific channel selection for classification of motor imagery electroencephalographic data, IEEE International Conference on Acoustics, Speech and Signal Processing, pp.1277-1280, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00837516

L. He, Y. Hu, Y. Li, and D. Li, Channel selection by Rayleigh coefficient maximization based genetic algorithm for classifying single-trial motor imagery EEG, Neurocomputing, vol.121, pp.423-433, 2013.

M. Arvaneh, C. Guan, K. K. Ang, and C. Quek, Optimizing the channel selection and classification accuracy in EEG-based BCI, IEEE Trans. Biomed. Eng, vol.58, pp.1865-1873, 2011.

O. Kyrgyzov, I. Bloch, Y. Yang, J. Wiart, and A. Souloumiac, Data ranking and clustering via normalized graph cut based on asymmetric affinity, Image Analysis and Processing-ICIAP 2013, pp.562-571, 2013.
URL : https://hal.archives-ouvertes.fr/cea-01831051

H. Shan, H. Xu, S. Zhu, and B. He, A novel channel selection method for optimal classification in different motor imagery BCI paradigms, Biomed. Eng. Online, vol.14, p.93, 2015.

J. Wang, F. Xue, and H. Li, Simultaneous channel and feature selection of fused EEG features based on Sparse Group Lasso, BioMed Res. Int, p.703768, 2015.

A. Barachant and S. Bonnet, Channel selection procedure using Riemannian distance for BCI applications, 5th International IEEE/EMBS Conference on Neural Engineering, pp.348-351, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00602707

Y. Yang, S. Chevallier, J. Wiart, and I. Bloch, Time-frequency optimization for discrimination between imagination of right and left hand movements based on two bipolar electroencephalography channels, EURASIP J. Adv. Signal Process, vol.2014, p.38, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01351618

Y. Yang, S. Chevallier, J. Wiart, and I. Bloch, Time-frequency selection in two bipolar channels for improving the classification of motor imagery EEG, 34th IEEE Annual International Conference of Engineering in Medicine and Biology Society, pp.2744-2747, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00737280

B. Yang, H. Li, Q. Wang, and Y. Zhang, Subject-based feature extraction by using fisher WPD-CSP in brain-computer interfaces, Comput. Methods Programs Biomed, vol.129, pp.21-28, 2016.

J. Luo, Z. Feng, J. Zhang, and N. Lu, Dynamic frequency feature selection based approach for classification of motor imageries, Comput. Biol. Med, vol.75, pp.45-53, 2016.

C. Ansuini, A. Cavallo, A. Koul, M. Jacono, Y. Yang et al., Predicting object size from hand kinematics: a temporal perspective, PLoS ONE, vol.10, p.120432, 2015.

A. Schlögl, F. Lee, H. Bischof, and G. Pfurtscheller, Characterization of four-class motor imagery EEG data for the BCI-competition, J. Neural Eng, vol.2, p.14, 2005.

G. Pfurtscheller, F. H. Lopes-da, and S. , Event-related EEG/MEG synchronization and desynchronization: basic principles, Clin. Neurophysiol, vol.110, pp.1842-1857, 1999.

B. Blankertz, K. R. Müller, D. J. Krusienski, G. Schalk, J. R. Wolpaw et al., The BCI competition III: validating alternative approaches to actual BCI problems, IEEE Trans. Neural Syst. Rehabil. Eng, vol.14, pp.153-159, 2006.

M. Grosse-wentrup and M. Buss, Multiclass common spatial patterns and information theoretic feature extraction, IEEE Trans. Biomed. Eng, vol.55, pp.1991-2000, 2008.

H. Wang, Harmonic mean of Kullback-Leibler divergences for optimizing multi-class EEG spatio-temporal filters, Neural Process. Lett, vol.36, pp.161-171, 2012.

M. Miao, H. Zeng, A. Wang, C. Zhao, and F. Liu, Discriminative spatial-frequency-temporal feature extraction and classification of motor imagery EEG: an sparse regression and weighted Naïve Bayesian classifier-based approach, J. Neurosci. Methods, vol.278, pp.13-24, 2017.

M. Mesbah, A. Khorshidtalab, H. Baali, and A. Al-ani, Motor imagery task classification using a signal-dependent orthogonal transform based feature extraction Neural Information Processing, vol.9490, pp.1-9, 2015.

H. Baali, A. Khorshidtalab, M. Mesbah, and M. J. Salami, A transform-based feature extraction approach for motor imagery tasks classification, IEEE J. Transl. Eng. Health Med, vol.3, pp.1-8, 2015.

C. Ansuini, A. Cavallo, and A. Koul, Grasping others' movements: rapid discrimination of object size from observed hand movements, J. Exp. Psychol. Hum. Percept. Perform, vol.42, issue.7, p.918, 2016.

J. Deng, J. Yao, and J. P. Dewald, Classification of the intention to generate a shoulder versus elbow torque by means of a time-frequency synthesized spatial patterns BCI algorithm, J. Neural Eng, vol.2, p.131, 2005.

D. M. Tax and R. P. Duin, Using two-class classifiers for multiclass classification International Conference on Pattern Recognition, vol.2, pp.124-127, 2002.

C. Vidaurre, N. Kramer, B. Blankertz, and A. Schlögl, Time domain parameters as a feature for EEG-based brain-computer interfaces, Neural Netw, vol.22, pp.1313-1319, 2009.

R. A. Johnson and D. W. Wichern, Applied Multivariate Statistical Analysis, 1992.

K. V. Mardia, Measures of multivariate skewness and kurtosis with applications, Biometrika, vol.57, pp.519-530, 1970.

H. Suk and S. Lee, A novel Bayesian framework for discriminative feature extraction in brain-computer interfaces, IEEE Trans. Pattern Anal. Mach. Intell, vol.35, pp.286-299, 2013.

F. Lotte and C. Guan, Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms, IEEE Trans. Biomed. Eng, vol.58, pp.355-362, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00476820

C. Park, D. Looney, N. Ur-rehman, A. Ahrabian, and D. P. Mandic, Classification of motor imagery BCI using multivariate empirical mode decomposition, IEEE Trans. Neural Syst. Rehabil. Eng, vol.21, pp.10-22, 2013.

A. Khorshidtalab, M. J. Salami, and A. Rini, Motor imagery task classification using transformation based features, Biomed. Signal Process. Control, vol.33, pp.213-219, 2017.

K. K. Ang, Z. Y. Chin, C. Wang, C. Guan, and H. Zhang, Filter bank common spatial pattern algorithm on BCI Competition IV datasets 2a and 2b, Front. Neurosci, vol.6, p.39, 2012.

B. Lou, B. Hong, X. Gao, and S. Gao, Bipolar electrode selection for a motor imagery based brain-computer interface, J. Neural Eng, vol.5, pp.342-349, 2008.

T. Solis-escalante, G. Müller-putz, and G. Pfurtscheller, Overt foot movement detection in one single Laplacian EEG derivation, J. Neurosci. Methods, vol.175, pp.148-153, 2008.

S. Fitzgibbon, D. Delosangeles, T. Lewis, D. Powers, E. Whitham et al., Surface Laplacian of scalp electrical signals and independent component analysis resolve EMG contamination of electroencephalogram, Int. J. Psychophysiol, vol.97, pp.277-284, 2015.

A. Schlögl, J. Kronegg, J. E. Huggins, and S. G. Mason, Toward Brain-Computer Interfacing, pp.327-360, 2007.

A. Jain and D. Zongker, Feature selection: evaluation, application, and small sample performance, IEEE Trans. Pattern Anal. Mach. Intell, vol.19, pp.153-158, 1997.

S. J. Raudys and A. K. Jain, Small sample size effects in statistical pattern recognition: recommendations for practitioners, IEEE Trans. Pattern Anal. Mach. Intell, vol.13, pp.252-264, 1991.

B. Hjorth, An on-line transformation of EEG scalp potentials into orthogonal source derivations, Electroencephalogr. Clin. Neurophysiol, vol.39, issue.5, pp.526-530, 1975.

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

E. Niedermeyer, F. H. Lopes-da, and S. , Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, 2005.

P. L. Nunez, R. Srinivasan, A. F. Westdorp, R. S. Wijesinghe, D. M. Tucker et al., EEG coherency: I: Statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales, Electroencephalogr Clin Neurophysiol, vol.103, issue.5, pp.499-515, 1997.

C. Vidaurre and B. Blankertz, Towards a cure for BCI illiteracy, Brain Topography, vol.23, issue.2, pp.194-198, 2010.

C. Jeunet, F. Jahanpour, and . Lotte, Why standard brain-computer interface (BCI) trainingprotocols should be changed: An experimental study, J Neural Eng, vol.13, 2016.

J. Faller, C. Vidaurre, T. Solis-escalante, C. Neuper, and R. Scherer, Autocalibration and recurrent adaptation: Towards a plug and play online ERD-BCI, IEEE Trans Neural Syst Rehabil Eng, vol.20, issue.3, pp.313-319, 2012.

F. Lotte, Signal processing approaches to minimize or suppress calibration time in oscillatory activity-based brain-computer interfaces, Proc IEEE, vol.103, issue.6, pp.871-890, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01159171

B. Rivet, A. Souloumiac, V. Attina, and G. Gibert, xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain-Computer Interface, IEEE Trans Biomed Eng, vol.56, issue.8, pp.2035-2043, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00454568

B. Blankertz, R. Tomioka, S. Lemm, M. Kawanabe, and K. R. Muller, Optimizing Spatial filters for Robust EEG Single-Trial Analysis, IEEE Signal Process Mag, vol.25, issue.1, pp.41-56, 2008.

G. Bin, X. Gao, Z. Yan, B. Hong, and S. Gao, An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method, J Neural Eng, vol.6, issue.4, 2009.

E. K. Kalunga, S. Chevallier, O. Rabreau, and E. Monacelli, Hybrid interface: Integrating BCI in multimodal human-machine interfaces, Int Conf on Adv Int Mech (AIM), pp.530-535, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01352056

F. Yger, M. Berar, and F. Lotte, Riemannian approaches in braincomputer interfaces: a review, IEEE Trans Neural Syst Rehabil Eng, vol.25, issue.10, pp.1753-1762, 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, Brain-Computer Interfaces, vol.4, pp.1-20, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01570120

A. Barachant, S. Bonnet, M. Congedo, and C. Jutten, Multiclass braincomputer interface classification by Riemannian geometry, IEEE Trans Biomed Eng, vol.59, issue.4, pp.920-928, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00681328

E. K. Kalunga, S. Chevallier, Q. Barthélemy, K. Djouani, E. Monacelli et al., Online SSVEP-based BCI using Riemannian geometry, Neurocomputing, vol.191, pp.55-68, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01681976

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.

P. T. Fletcher, C. Lu, S. M. Pizer, and S. Joshi, Principal geodesic analysis for the study of nonlinear statistics of shape, IEEE Trans Med Imaging, vol.23, issue.8, pp.995-1005, 2004.

H. He and . Wu, Transfer learning enhanced common spatial pattern filtering for brain computer interfaces (BCIs): Overview and a new approach, pp.811-821, 2017.

V. N-r-waytowich, A. Lawhern, K. Bohannon, B. Ball, and . Lance, Spectral transfer learning using information geometry for a user-independent brain-computer interface, Frontiers in Neuroscience, vol.10, p.430, 2016.

H. Kang, Y. Nam, and S. Choi, Composite common spatial pattern for subject-to-subject transfer, IEEE Signal Process Lett, vol.16, issue.8, pp.683-686, 2009.

F. Lotte and C. Guan, Regularizing common spatial patterns to improve BCI designs: Unified theory and new algorithms, IEEE Trans Biomed Eng, vol.58, pp.355-362, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00476820

O. Ledoit and M. Wolf, A well-conditioned estimator for largedimensional covariance matrices, Journal of Multivariate Analysis, vol.88, issue.2, pp.365-411, 2004.