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

S. N. Abdulkader, A. Atia, and M. ,

. Mostafa, Brain computer interfacing: Applications and challenges, Egypt. Inform. J, vol.16, issue.2, pp.213-230, 2015.

S. Perdikis, L. Tonin, S. Saeedi, C. Schneider, and J. Del-r.-millán, The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users, PLoS Biol, vol.16, issue.5, 2018.

D. J. Mcfarland and J. R. Wolpaw, Braincomputer interface use is a skill that user and system acquire together, PLOS Biol, vol.16, issue.7, p.2006719, 2018.

D. J. Mcfarland, A. T. Lefkowicz, and J. ,

. Wolpaw, Design and operation of an EEG-based braincomputer interface with digital signal processing technology, Behav. Res. Methods Instrum. Comput, vol.29, issue.3, pp.337-345, 1997.

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

M. Ahn and S. C. Jun, Performance variation in motor imagery brain-computer interface: A brief review, J. Neurosci. Methods, vol.243, pp.103-110, 2015.

C. Jeunet, B. N'kaoua, S. Subramanian, and M. ,

F. Hachet and . Lotte, Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns, PLoS ONE, vol.10, issue.12, p.143962, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01177685

E. M. Hammer, Psychological predictors of SMR-BCI performance, Biol Psychol, vol.89, issue.1, pp.80-86, 2012.

A. Vuckovic and B. A. Osuagwu, Using a motor imagery questionnaire to estimate the performance of a Brain-Computer Interface based on object oriented motor imagery, Clin. Neurophysiol. Off. J. Int. Fed. Clin. Neurophysiol, vol.124, issue.8, pp.1586-1595, 2013.

E. M. Hammer, T. Kaufmann, S. C. Kleih, and B. ,

A. Blankertz and . Kübler, Visuo-motor coordination ability predicts performance with brain-computer interfaces controlled by modulation of sensorimotor rhythms (SMR), Front Hum Neurosci, vol.8, 2014.

B. Blankertz, Neurophysiological predictor of SMR-based BCI performance

, NeuroImage, vol.51, issue.4, pp.1303-1309, 2010.

M. Ahn, H. Cho, S. Ahn, and S. C. Jun, High theta and low alpha powers may be indicative of BCIilliteracy in motor imagery, PLoS ONE, vol.8, issue.11, p.80886, 2013.

M. Grosse-wentrup, B. Schölkopf, and J. Hill, Causal influence of gamma oscillations on the sensorimotor rhythm, Neuroimage, vol.56, issue.2, pp.837-842, 2011.

M. Grosse-wentrup and B. Schölkopf, High ?-power predicts performance in sensorimotor-rhythm brain-computer interfaces, J Neural Eng, vol.9, issue.4, p.46001, 2012.

J. R. Wolpaw, D. J. Mcfarland, T. M. Vaughan, and G. Schalk, The Wadsworth Center brain-computer interface (BCI) research and development program, IEEE Trans. Neural Syst. Rehabil. Eng. Publ. IEEE Eng

, Med. Biol. Soc, vol.11, issue.2, pp.204-207, 2003.

F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, and B. Arnaldi, A review of classification algorithms for EEG-based brain-computer interfaces, J Neural Eng, vol.4, issue.2, pp.1-13, 2007.
URL : https://hal.archives-ouvertes.fr/hal-01846433

R. Oostenveld, P. Fries, E. Maris, and J. ,

. Schoffelen, FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data, p.156869, 2010.

G. Schalk, D. J. Mcfarland, T. Hinterberger, N. Birbaumer, and J. R. Wolpaw, BCI2000: a generalpurpose brain-computer interface (BCI) system, IEEE Trans. Biomed. Eng, vol.51, issue.6, pp.1034-1043, 2004.

A. J. Bell and T. J. Sejnowski, An informationmaximization approach to blind separation and blind deconvolution, Neural Comput, vol.7, issue.6, pp.1129-1159, 1995.

M. Fuchs, M. Wagner, and J. Kastner, Boundary element method volume conductor models for EEG source reconstruction, Clin. Neurophysiol, vol.112, issue.8, pp.1400-1407, 2001.

A. Gramfort, T. Papadopoulo, E. Olivi, and M. ,

. Clerc, OpenMEEG: opensource software for quasistatic bioelectromagnetics, Biomed. Eng. OnLine, vol.9, p.45, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00467061

F. Lin, T. Witzel, S. P. Ahlfors, and S. ,

J. W. Stufflebeam, M. S. Belliveau, and . Hämäläinen, Assessing and improving the spatial accuracy in MEG source localization by depth-weighted minimum-norm estimates, NeuroImage, vol.31, issue.1, pp.160-171, 2006.

F. Tadel, S. Baillet, J. C. Mosher, D. Pantazis, and R. M. Leahy, Brainstorm: A User-Firendly Application for MEG/EEG Analysis, Comput. Intell. Neurosci, vol.2011, 2011.

C. Destrieux, B. Fischl, A. Dale, and E. Halgren, Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature, NeuroImage, vol.53, issue.1, pp.1-15, 2010.

A. Mottaz, Modulating functional connectivity after stroke with neurofeedback: Effect on motor deficits in a controlled cross-over study, NeuroImage Clin, vol.20, pp.336-346, 2018.

D. S. Bassett and A. N. Khambhati, A network engineering perspective on probing and perturbing cognition with neurofeedback, Ann. N. Y. Acad. Sci, vol.1396, issue.1, pp.126-143, 2017.

F. De-vico-fallani and D. S. Bassett, Network neuroscience for optimizing brain-computer interfaces, Phys. Life Rev, 2019.

G. Nolte, O. Bai, L. Wheaton, Z. Mari, S. Vorbach et al., Identifying true brain interaction from EEG data using the imaginary part of coherency, Clin Neurophysiol, vol.115, issue.10, pp.2292-2307, 2004.

K. Sekihara, J. P. Owen, S. Trisno, and S. S. Nagarajan, Removal of Spurious Coherence in MEG Source-Space Coherence Analysis, IEEE Trans

, Biomed. Eng, vol.58, issue.11, pp.3121-3129, 2011.

W. Klimesch, EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis, Brain Res. Rev, vol.29, issue.2, pp.169-195, 1999.

R. Sigala, S. Haufe, D. Roy, H. R. Dinse, and P. ,

. Ritter, The role of alpha-rhythm states in perceptual learning: insights from experiments and computational models, Front. Comput. Neurosci, vol.8, 2014.

E. G. Antzoulatos and E. K. Miller, Increases in functional connectivity between prefrontal cortex and striatum during category learning, Neuron, vol.83, issue.1, pp.216-225, 2014.

R. Oostenveld, FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data, FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data, p.156869, 2010.

T. Cattai, S. Colonnese, M. Corsi, and D. S. ,

G. Bassett, F. Scarano, . De-vico, and . Fallani, Characterization of mental states through node connectivity between brain signals, presented at the European Signal Processing Conference, pp.1391-1395, 2018.

F. Lotte, F. Larrue, and C. Mühl, Flaws in current human training protocols for spontaneous BrainComputer Interfaces: lessons learned from instructional design, Front Hum Neurosci, vol.7, p.568, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00862716

M. Corsi, Integrating EEG and MEG signals to improve motor imagery classification in braincomputer interface, Int. J. Neural Syst, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01893132

J. Toppi, Different Topological Properties of EEG-Derived Networks Describe Working Memory Phases as Revealed by Graph Theoretical Analysis, Front. Hum. Neurosci, vol.11, 2018.

M. Corsi, Spatiotemporal neural correlates of brain-computer interface learning, p.487074, 2018.