P. J. Allen, O. Josephs, T. , and R. , A Method for Removing Imaging Artifact from Continuous EEG Recorded during Functional MRI, NeuroImage, vol.12, issue.2, pp.230-239, 2000.
DOI : 10.1006/nimg.2000.0599

P. J. Allen, G. Polizzi, K. Krakow, D. R. Fish, and L. Lemieux, Identification of EEG Events in the MR Scanner: The Problem of Pulse Artifact and a Method for Its Subtraction, NeuroImage, vol.8, issue.3, pp.229-2390361, 1998.
DOI : 10.1006/nimg.1998.0361

S. Amiri, R. Fazel-rezai, and V. Asadpour, A Review of Hybrid Brain-Computer Interface Systems, Advances in Human-Computer Interaction, vol.9, issue.50, pp.1-8, 2013.
DOI : 10.1016/j.jneumeth.2007.03.005

S. Baillet, J. C. Mosher, and R. M. Leahy, Electromagnetic brain mapping, IEEE Signal Processing Magazine, vol.18, issue.6, pp.14-30, 2001.
DOI : 10.1109/79.962275

N. Birbaumer, R. Murguialday, A. Weber, C. Montoya, P. Birbaumer et al., Neurofeedback and brain-computer interface clinical applications Learned regulation of brain metabolism, Int. Rev. Neurobiol. Trends Cogn. Sci, vol.8686008, issue.17, pp.107-117, 2009.

B. Blankertz, R. Tomioka, S. Lemm, M. Kawanabe, and K. Muller, Optimizing Spatial filters for Robust EEG Single-Trial Analysis, IEEE Signal Processing Magazine, vol.25, issue.1, pp.41-56, 2008.
DOI : 10.1109/MSP.2008.4408441

A. P. Buccino, H. O. Keles, and A. Omurtag, Hybrid EEG-fNIRS Asynchronous Brain-Computer Interface for Multiple Motor Tasks, PLOS ONE, vol.8, issue.2, 2016.
DOI : 10.1371/journal.pone.0146610.t003

L. Carlucci, C. , and J. , On the Necessity of U-Shaped Learning, Topics in Cognitive Science, vol.412, issue.1, pp.56-88, 2013.
DOI : 10.1111/tops.12002

U. Chaudhary, N. Birbaumer, and A. Ramos-murguialday, Brain???computer interfaces for communication and rehabilitation, Nature Reviews Neurology, vol.74, issue.9, pp.513-525, 2016.
DOI : 10.1038/nrneurol.2016.113

M. Chiew, S. M. Laconte, G. , and S. J. , Investigation of fMRI neurofeedback of differential primary motor cortex activity using kinesthetic motor imagery, NeuroImage, vol.61, issue.1, 2012.
DOI : 10.1016/j.neuroimage.2012.02.053

S. H. Choi, M. Lee, Y. Wang, H. , and B. , Estimation of Optimal Location of EEG Reference Electrode for Motor Imagery Based BCI Using fMRI, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, pp.1193-1196, 2006.
DOI : 10.1109/IEMBS.2006.260270

L. Dong, D. Gong, P. A. Valdes-sosa, Y. Xia, C. Luo et al., Simultaneous EEG-fMRI: Trial level spatio-temporal fusion for hierarchically reliable information discovery, NeuroImage, vol.99, pp.28-41, 2014.
DOI : 10.1016/j.neuroimage.2014.05.029

S. Fazli, J. Mehnert, J. Steinbrink, G. Curio, A. Villringer et al., Enhanced performance by a hybrid NIRS???EEG brain computer interface, NeuroImage, vol.59, issue.1, pp.519-529, 2012.
DOI : 10.1016/j.neuroimage.2011.07.084

S. T. Foldes, D. J. Weber, J. L. Collinger, T. W. Wilson, A. Fleischer et al., MEG-based neurofeedback for hand rehabilitation, Journal of NeuroEngineering and Rehabilitation, vol.1, issue.February, pp.85-95, 2015.
DOI : 10.1186/s12984-015-0076-7

E. Formaggio, S. F. Storti, R. Cerini, A. Fiaschi, and P. Manganotti, Brain oscillatory activity during motor imagery in EEG-fMRI coregistration, Magnetic Resonance Imaging, vol.28, issue.10, pp.1403-1412, 2010.
DOI : 10.1016/j.mri.2010.06.030

A. Gaume, A. Vialatte, A. Mora-sánchez, C. Ramdani, and F. B. Vialatte, A psychoengineering paradigm for the neurocognitive mechanisms of biofeedback and neurofeedback, Neuroscience & Biobehavioral Reviews, vol.68, pp.891-910, 2016.
DOI : 10.1016/j.neubiorev.2016.06.012

R. Grech, T. Cassar, J. Muscat, K. P. Camilleri, S. G. Fabri et al., Review on solving the inverse problem in EEG source analysis, Journal of NeuroEngineering and Rehabilitation, vol.5, issue.1, 2008.
DOI : 10.1186/1743-0003-5-25

J. H. Gruzelier, EEG-neurofeedback for optimising performance. I: A review of cognitive and affective outcome in healthy participants, Neuroscience & Biobehavioral Reviews, vol.44, 2014.
DOI : 10.1016/j.neubiorev.2013.09.015

J. H. Gruzelier, EEG-neurofeedback for optimising performance. II: Creativity, the performing arts and ecological validity, Neuroscience & Biobehavioral Reviews, vol.44, 2014.
DOI : 10.1016/j.neubiorev.2013.11.004

D. C. Hammond, What is Neurofeedback: An Update, Journal of Neurotherapy, vol.15, issue.4, pp.305-336, 2011.
DOI : 10.1080/10874208.2011.623090

Y. Jeon, C. S. Nam, Y. J. Kim, and M. C. Whang, Event-related (De)synchronization (ERD/ERS) during motor imagery tasks: Implications for brain???computer interfaces, International Journal of Industrial Ergonomics, vol.41, issue.5, pp.428-436, 2011.
DOI : 10.1016/j.ergon.2011.03.005

J. Jorge, F. Grouiller, R. Gruetter, W. Van-der-zwaag, and P. Figueiredo, Towards high-quality simultaneous EEG-fMRI at 7 T: Detection and reduction of EEG artifacts due to head motion, NeuroImage, vol.120, pp.143-153, 2015.
DOI : 10.1016/j.neuroimage.2015.07.020

J. Jorge, W. Van-der-zwaag, and P. Figueiredo, EEG???fMRI integration for the study of human brain function, NeuroImage, vol.102, 2013.
DOI : 10.1016/j.neuroimage.2013.05.114

M. Kleiner, D. Brainard, and D. G. Pelli, What's new in psychtoolbox-3, Perception, vol.36, 2007.

S. E. Kober, G. Wood, J. Kurzmann, E. V. Friedrich, M. Stangl et al., Near-infrared spectroscopy based neurofeedback training increases specific motor imagery related cortical activation compared to sham feedback, Biological Psychology, vol.95, 2014.
DOI : 10.1016/j.biopsycho.2013.05.005

P. Krishnaswamy, G. Bonmassar, C. Poulsen, E. T. Pierce, P. L. Purdon et al., Reference-free removal of EEG-fMRI ballistocardiogram artifacts with harmonic regression, NeuroImage, vol.128, pp.398-412, 2016.
DOI : 10.1016/j.neuroimage.2015.06.088

S. L. Liew, M. Rana, S. Cornelsen, M. F. De-barros-filho, N. Birbaumer et al., Improving Motor Corticothalamic Communication After Stroke Using Real-Time fMRI Connectivity-Based Neurofeedback, Neurorehabilitation and Neural Repair, vol.26, issue.1, pp.671-675, 2015.
DOI : 10.1002/ana.410290112

D. E. Linden, Neurofeedback and Networks of Depression, Dialogues Clin. Neurosci, vol.16, pp.103-112, 2014.

D. E. Linden, T. , and D. L. , Real-time functional magnetic resonance imaging neurofeedback in motor neurorehabilitation, Current Opinion in Neurology, vol.29, issue.4, pp.412-418, 2016.
DOI : 10.1097/WCO.0000000000000340

M. Mano, A. Lécuyer, E. Bannier, L. Perronnet, S. Noorzadeh et al., How to Build a Hybrid Neurofeedback Platform Combining EEG and fMRI, Frontiers in Neuroscience, vol.63, 2017.
DOI : 10.1016/j.neuroimage.2012.07.031

S. Marchesotti, M. Bassolino, A. Serino, H. Bleuler, and O. Blanke, Quantifying the role of motor imagery in brain-machine interfaces, Scientific Reports, vol.86, issue.1, 2016.
DOI : 10.1016/0013-4694(93)90110-H

C. Maumet, From Group to Patient-Specific Analysis of Brain Function in Arterial Spin Labelling and BOLD Functional MRI Available online at: https, 2013.

A. Mayeli, V. Zotev, H. Refai, and J. Bodurka, Real-time EEG artifact correction during fMRI using ICA, Journal of Neuroscience Methods, vol.274, 2016.
DOI : 10.1016/j.jneumeth.2016.09.012

URL : http://doi.org/10.1016/j.jneumeth.2016.09.012

M. Mihara, N. Hattori, and M. Hatakenaka, Near-infrared Spectroscopy-mediated Neurofeedback Enhances Efficacy of Motor Imagery-based Training in Poststroke Victims: A Pilot Study, Stroke, vol.44, issue.4, pp.1091-1098, 2013.
DOI : 10.1161/STROKEAHA.111.674507

M. Mihara, I. Miyai, N. Hattori, M. Hatakenaka, H. Yagura et al., Neurofeedback Using Real-Time Near-Infrared Spectroscopy Enhances Motor Imagery Related Cortical Activation, PLoS ONE, vol.41, issue.3, 2012.
DOI : 10.1371/journal.pone.0032234.t004

URL : http://doi.org/10.1371/journal.pone.0032234

T. Murta, U. J. Chaudhary, T. Tierney, A. Dias, M. Leite et al., Phase???amplitude coupling and the BOLD signal: A simultaneous intracranial EEG (icEEG) - fMRI study in humans performing a finger-tapping task, NeuroImage, vol.146, pp.438-451, 2016.
DOI : 10.1016/j.neuroimage.2016.08.036

T. Murta, M. Leite, D. W. Carmichael, P. Figueiredo, and L. Lemieux, Electrophysiological correlates of the BOLD signal for EEG-informed fMRI, Human Brain Mapping, vol.130, issue.Pt 9, pp.391-414, 2015.
DOI : 10.1002/hbm.22623

C. Neuper, M. Wörtz, and G. Pfurtscheller, ERD/ERS patterns reflecting sensorimotor activation and deactivation, Prog. Brain Res, vol.159, issue.06, pp.211-222, 2006.
DOI : 10.1016/S0079-6123(06)59014-4

L. Perronnet, A. Lécuyer, F. Lotte, M. Clerc, and C. Barillot, Brain Training with Neurofeedback, Brain?Computer Interfaces 1: Foundations and Methods, pp.271-292, 2016.
DOI : 10.1002/9781119144977.ch13

URL : https://hal.archives-ouvertes.fr/hal-01413424

G. Pfurtscheller, B. Z. Allison, C. Brunner, G. Bauernfeind, T. Solis-escalante et al., The hybrid BCI, Frontiers in Neuroscience, 2010.
DOI : 10.3389/fnpro.2010.00003

G. Pfurtscheller and C. Neuper, Motor imagery and direct brain-computer communication, Proc. IEEE, pp.1123-1134, 2001.
DOI : 10.1109/5.939829

F. Pichiorri, G. Morone, M. Petti, J. Toppi, I. Pisotta et al., Brain-computer interface boosts motor imagery practice during stroke recovery, Annals of Neurology, vol.65, issue.pt 1, pp.851-865, 2015.
DOI : 10.1002/ana.24390

H. Ramoser, J. Müller-gerking, and G. Pfurtscheller, Optimal spatial filtering of single trial EEG during imagined hand movement, IEEE Transactions on Rehabilitation Engineering, vol.8, issue.4, pp.441-446, 2000.
DOI : 10.1109/86.895946

A. Ramos-murguialday, D. Broetz, M. Rea, L. Läer, O. Yilmaz et al., Brain-machine interface in chronic stroke rehabilitation: A controlled study, Annals of Neurology, vol.10, issue.1, pp.100-108, 2013.
DOI : 10.1002/ana.23879

A. K. Rehme, S. B. Eickhoff, C. Rottschy, G. R. Fink, and C. Grefkes, Activation likelihood estimation meta-analysis of motor-related neural activity after stroke, NeuroImage, vol.59, issue.3, pp.2771-2482, 2012.
DOI : 10.1016/j.neuroimage.2011.10.023

S. Rimbert, L. Bougrain, C. Lindig-león, and G. Serrière, Amplitude and Latency of Beta Power during a Discrete and Continuous Motor Imageries. Inria. Available online at, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01152205

F. Scharnowski and N. Weiskopf, Cognitive enhancement through real-time fMRI neurofeedback, Current Opinion in Behavioral Sciences, vol.4, pp.122-149, 2015.
DOI : 10.1016/j.cobeha.2015.05.001

N. Sharma, V. M. Pomeroy, J. C. Baron, K. Shindo, K. Kawashima et al., Motor imagery: a backdoor to the motor system after stroke? Stroke 37 Effects of neurofeedback training with an electroencephalogrambased brain-computer interface for hand paralysis in patients with chronic stroke: a preliminary case series study, J. Rehabil. Med, vol.43, pp.951-957, 1941.

S. Silvoni, A. Ramos-murguialday, M. Cavinato, C. Volpato, G. Cisotto et al., Brain-Computer Interface in Stroke: A Review of Progress, Clinical EEG and Neuroscience, vol.8, issue.2, pp.245-252, 1177.
DOI : 10.1056/NEJMoa0911341

R. Sitaram, A. Caria, R. Veit, T. Gaber, G. Rota et al., fMRI Brain-Computer Interface: A Tool for Neuroscientific Research and Treatment, Computational Intelligence and Neuroscience, vol.39, issue.1, pp.25487-25497, 2007.
DOI : 10.1002/1522-2594(200009)44:3<457::AID-MRM17>3.0.CO;2-R

R. Sitaram, R. Veit, B. Stevens, A. Caria, C. Gerloff et al., Acquired Control of Ventral Premotor Cortex Activity by Feedback Training, Neurorehabilitation and Neural Repair, vol.21, issue.3, pp.256-265, 1177.
DOI : 10.1113/jphysiol.1993.sp019912

S. R. Soekadar, N. Birbaumer, and L. G. Cohen, Brain???Computer Interfaces in the Rehabilitation of Stroke and Neurotrauma, Systems Neuroscience and Rehabilitation, pp.3-18, 2011.
DOI : 10.1007/978-4-431-54008-3_1

G. Sudre, L. Parkkonen, E. Bock, S. Baillet, W. Wang et al., rtMEG: A Real-Time Software Interface for Magnetoencephalography, Computational Intelligence and Neuroscience, vol.104, issue.5, pp.327953-327963, 2011.
DOI : 10.1023/B:BRAT.0000032864.93890.f9

J. Sulzer, S. Haller, F. Scharnowski, N. Weiskopf, N. Birbaumer et al., Real-time fMRI neurofeedback: Progress and challenges, NeuroImage, vol.76, pp.386-399, 2013.
DOI : 10.1016/j.neuroimage.2013.03.033

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4878436

R. T. Thibault, M. Lifshitz, and A. Raz, The self-regulating brain and neurofeedback: Experimental science and clinical promise, Cortex, vol.74, 2016.
DOI : 10.1016/j.cortex.2015.10.024

J. N. Van-der-meer, A. Pampel, E. J. Van-someren, J. R. Ramautar, Y. D. Van-der-werf et al., Carbon-wire loop based artifact correction outperforms post-processing EEG/fMRI corrections???A validation of a real-time simultaneous EEG/fMRI correction method, NeuroImage, vol.125, pp.880-894, 2016.
DOI : 10.1016/j.neuroimage.2015.10.064

D. J. Vernon, Can Neurofeedback Training Enhance Performance? An Evaluation of the Evidence with Implications for Future Research, Applied Psychophysiology and Biofeedback, vol.29, issue.1, pp.347-364, 2005.
DOI : 10.1007/s10484-005-8421-4

C. K. Wong, V. Zotev, M. Misaki, R. Phillips, Q. Luo et al., Automatic EEG-assisted retrospective motion correction for fMRI (aE-REMCOR), NeuroImage, vol.129, pp.133-147, 2016.
DOI : 10.1016/j.neuroimage.2016.01.042

URL : http://doi.org/10.1016/j.neuroimage.2016.01.042

X. Wu, T. Wu, Z. Zhan, L. Yao, W. et al., A real-time method to reduce ballistocardiogram artifacts from EEG during fMRI based on optimal basis sets (OBS), Computer Methods and Programs in Biomedicine, vol.127, pp.114-125, 2016.
DOI : 10.1016/j.cmpb.2016.01.018

S. Wyckoff and N. Birbaumer, Neurofeedback and Brain-Computer Interfaces, The Handbook of Behavioral Medicine, pp.275-312, 2014.
DOI : 10.1002/9781118453940.ch15

D. Yao, A method to standardize a reference of scalp EEG recordings to a point at infinity, Physiological Measurement, vol.22, issue.4, pp.693-711, 2001.
DOI : 10.1088/0967-3334/22/4/305

S. Yin, Y. Liu, and M. Ding, Amplitude of Sensorimotor Mu Rhythm Is Correlated with BOLD from Multiple Brain Regions: A Simultaneous EEG-fMRI Study, Frontiers in Human Neuroscience, vol.20, issue.RC63, 2016.
DOI : 10.1371/journal.pone.0002001

H. Yuan, T. Liu, R. Szarkowski, C. Rios, J. Ashe et al., Negative covariation between task-related responses in alpha/beta-band activity and BOLD in human sensorimotor cortex: An EEG and fMRI study of motor imagery and movements, NeuroImage, vol.49, issue.3, pp.2596-2606, 2010.
DOI : 10.1016/j.neuroimage.2009.10.028

C. Zich, S. Debener, C. Kranczioch, M. G. Bleichner, I. Gutberlet et al., Real-time EEG feedback during simultaneous EEG???fMRI identifies the cortical signature of motor imagery, NeuroImage, vol.114, pp.438-447, 2015.
DOI : 10.1016/j.neuroimage.2015.04.020