D. F. Abbott, R. A. Masterton, J. S. Archer, S. W. Fleming, A. E. Warren et al., Constructing Carbon Fiber Motion-Detection Loops for Simultaneous EEG-fMRI. Frontiers pre-print in neurology 5, p.260, 2014.

O. Alkoby, A. Abu-rmileh, O. Shriki, and D. Todder, Can We Predict Who Will Respond to Neurofeedback? A Review of the Inefficacy Problem and Existing Predictors for Successful EEG Neurofeedback Learning, Neuroscience, vol.378, pp.155-164, 2018.

P. J. Allen, O. Josephs, and R. Turner, A method for removing imaging artifact from continuous EEG recorded during functional MRI, NeuroImage, vol.12, pp.230-239, 2000.

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, pp.229-239, 1998.

M. Arns, J. Batail, S. Bioulac, M. Congedo, C. Daudet et al., Neurofeedback: One of today's techniques in psychiatry?, L'Encéphale, vol.43, pp.135-145, 2017.

S. Baillet, J. C. Mosher, and R. M. Leahy, Electromagnetic brain mapping, IEEE Signal Processing Magazine, vol.18, 2001.

F. Biessmann, S. Plis, F. C. Meinecke, T. Eichele, and K. Muller, Analysis of Multimodal Neuroimaging Data, IEEE Reviews in Biomedical Engineering, vol.4, pp.26-58, 2011.

N. Birbaumer, A. Ramos-murguialday, C. Weber, and P. Montoya, Chapter 8 Neurofeedback and Brain-Computer Interface, In International Review of Neurobiology, vol.86, pp.107-117, 2009.

N. Birbaumer, S. Ruiz, and R. Sitaram, Learned regulation of brain metabolism, Trends in cognitive sciences, vol.17, pp.295-302, 2013.

E. Buch, C. Weber, L. G. Cohen, C. Braun, M. A. Dimyan et al., Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke, Stroke; a journal of cerebral circulation, vol.39, pp.910-917, 2008.

C. A. Buneo and R. A. Andersen, The posterior parietal cortex: Sensorimotor interface for the planning and online control of visually guided movements, Neuropsychologia, vol.44, pp.2594-2606, 2006.

R. Chavarriaga, M. Fried-oken, S. Kleih, F. Lotte, and R. Scherer, Heading for new shores! Overcoming pitfalls in BCI design, Brain-Computer Interfaces, vol.4, pp.1-14, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01415906

M. E. Chowdhury, K. J. Mullinger, P. Glover, and R. Bowtell, Reference layer artefact subtraction (RLAS): A novel method of minimizing EEG artefacts during simultaneous fMRI, NeuroImage, vol.84, pp.307-319, 2014.

L. Confalonieri, G. Pagnoni, L. W. Barsalou, J. Rajendra, S. B. Eickhoff et al., , 2012.

, Brain Activation in Primary Motor and Somatosensory Cortices during Motor Imagery Correlates with Motor Imagery Ability in Stroke Patients, ISRN Neurology, vol.2012, pp.1-17

J. C. Culham and N. G. Kanwisher, Neuroimaging of cognitive functions in human parietal cortex, Current Opinion in Neurobiology, vol.11, pp.157-163, 2001.

C. Cury, P. Maurel, R. Gribonval, and C. Barillot, A Sparse EEG-Informed fMRI Model for Hybrid EEG-fMRI Neurofeedback Prediction, Frontiers in Neuroscience, vol.13, p.1451, 2020.
URL : https://hal.archives-ouvertes.fr/inserm-02090676

J. Danckert, S. Ferber, T. Doherty, H. Steinmetz, D. Nicolle et al., Selective, Nonlateralized Impairment of Motor Imagery Following Right Parietal Damage, Neurocase, vol.8, pp.194-204, 2002.

S. Darvishi, A. Gharabaghi, C. B. Boulay, M. C. Ridding, D. Abbott et al., , 2017.

, Proprioceptive feedback facilitates motor imagery-related operant learning of sensorimotor beta-band modulation, Frontiers in Neuroscience, vol.11, p.60

M. De-vos, R. Zink, B. Hunyadi, B. Mijovic, S. V. Huffel et al., The quest for single trial correlations in multimodal EEG-fMRI data, Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), p.6027, 2013.

K. Emmert, R. Kopel, Y. Koush, R. Maire, P. Senn et al., Continuous vs. intermittent neurofeedback to regulate auditory cortex activity of tinnitus patients using real-time fMRI -A pilot study, NeuroImage: Clinical, vol.14, pp.97-104, 2017.

K. Emmert, R. Kopel, J. Sulzer, A. B. Brühl, B. D. Berman et al., , 2016.

, Meta-analysis of real-time fMRI neurofeedback studies using individual participant data: How is brain regulation mediated?, NeuroImage, vol.124, pp.806-812

S. Fazli, S. Dähne, W. Samek, F. Bießmann, and K. Müller, Learning From More Than One Data Source: Data Fusion Techniques for Sensorimotor Rhythm-Based Brain-Computer Interfaces, Proceedings of the IEEE 103, pp.891-906, 2015.

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, pp.519-529, 2012.

M. C. Fellner, G. Volberg, K. J. Mullinger, M. Goldhacker, M. Wimber et al., Spurious correlations in simultaneous EEG-fMRI driven by in-scanner movement, NeuroImage, vol.133, pp.354-366, 2016.

M. K. Fleming, C. M. Stinear, and W. D. Byblow, Bilateral parietal cortex function during motor imagery, Experimental Brain Research, vol.201, pp.499-508, 2010.

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, pp.1403-1412, 2010.

A. Gaume, A. Vialatte, A. Mora-sánchez, C. Ramdani, and F. Vialatte, A psychoengineering paradigm for the neurocognitive mechanisms of biofeedback and neurofeedback, Neuroscience & Biobehavioral Reviews, 2016.

E. Gerardin, A. Sirigu, S. Lehéricy, J. B. Poline, B. Gaymard et al., Partially overlapping neural networks for real and imagined hand movements, Cerebral cortex, vol.10, pp.1093-104, 1991.
URL : https://hal.archives-ouvertes.fr/hal-00349826

S. I. Gonçalves, J. C. De-munck, P. J. Pouwels, R. Schoonhoven, J. P. Kuijer et al., Correlating the alpha rhythm to BOLD using simultaneous EEG/fMRI: Inter-subject variability, NeuroImage, vol.30, pp.203-213, 2006.

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, p.25, 2008.

J. H. Gruzelier, EEG-neurofeedback for optimising performance. I: a review of cognitive and affective outcome in healthy participants, Neuroscience and biobehavioral reviews, vol.44, pp.124-141, 2014.

A. Guillot, C. Collet, V. A. Nguyen, F. Malouin, C. Richards et al., Brain activity during visual versus kinesthetic imagery: An fMRI study, Human Brain Mapping, vol.30, pp.2157-2172, 2009.

D. Hammond, What is neurofeedback: An update, Journal of Neurotherapy, vol.15, pp.305-336, 2011.

T. Hanakawa, I. Immisch, K. Toma, M. A. Dimyan, P. Van-gelderen et al., Functional Properties of Brain Areas Associated With Motor Execution and Imagery, Journal of Neurophysiology, vol.89, pp.989-1002, 2002.

R. J. Huster, Z. N. Mokom, S. Enriquez-geppert, and C. S. Herrmann, Braincomputer interfaces for EEG neurofeedback: peculiarities and solutions, International journal of psychophysiology : official journal of the International Organization of Psychophysiology, vol.91, pp.36-45, 2014.

S. Hétu, M. Grégoire, A. Saimpont, M. Coll, F. Eugène et al., The neural network of motor imagery: An ALE meta-analysis, Neuroscience & Biobehavioral Reviews, vol.37, pp.930-949, 2013.

M. Jansen, T. P. White, K. J. Mullinger, E. B. Liddle, P. A. Gowland et al., , 2012.

, Motion-related artefacts in EEG predict neuronally plausible patterns of activation in fMRI data, NeuroImage, vol.59, pp.261-270

C. Jeunet, F. Lotte, J. Batail, P. Philip, M. Franchi et al., 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

C. Jeunet, B. N'kaoua, S. Subramanian, M. Hachet, L. et al., Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns, PLOS ONE, vol.10, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01177685

C. Jeunet, C. Vi, D. Spelmezan, B. N'kaoua, F. Lotte et al., , 2015.

, Continuous Tactile Feedback for Motor-Imagery Based Brain-Computer Interaction in a Multitasking Context, Human-Computer Interaction (INTERACT)

J. Jorge, W. Van-der-zwaag, and P. Figueiredo, EEG-fMRI integration for the study of human brain function, NeuroImage, vol.102, pp.24-34, 2014.

C. H. Kasess, C. Windischberger, R. Cunnington, R. Lanzenberger, L. Pezawas et al., , 2008.

, The suppressive influence of SMA on M1 in motor imagery revealed by fMRI and dynamic causal modeling, NeuroImage, vol.40, pp.828-837

T. Kaufmann and J. Williamson, Visually multimodal vs. classic unimodal feedback approach for smr-bcis: a comparison study, Int. J, vol.13, pp.80-81, 2011.

M. Kleiner, D. Brainard, D. Pelli, A. Ingling, R. Murray et al., What's new in Psychtoolbox-3, Perception, vol.36, p.1, 2007.

S. E. Kober, M. Witte, M. Ninaus, C. Neuper, and G. Wood, Learning to modulate one's own brain activity: the effect of spontaneous mental strategies, Frontiers in Human Neuroscience, vol.7, p.695, 2013.

S. E. Kober, G. Wood, J. Kurzmann, E. V. Friedrich, M. Stangl et al., Nearinfrared spectroscopy based neurofeedback training increases specific motor imagery related cortical activation compared to sham feedback, Biological Psychology, vol.95, 2014.

R. Kopel, K. Emmert, F. Scharnowski, S. Haller, and D. Van-de-ville, Distributed patterns of brain activity underlying real-time fMRI neurofeedback training, IEEE Transactions on Biomedical Engineering, vol.64, pp.1-1, 2016.

F. Krause, C. Benjamins, M. Lührs, J. Eck, Q. Noirhomme et al., Real-time fMRI-based self-regulation of brain activation across different visual feedback presentations, Brain-Computer Interfaces, 2017.

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.

D. Lahat, T. Adali, J. , and C. , Multimodal Data Fusion : An Overview of Methods , Challenges , and Prospects. Proceedings of the IEEE 103, pp.1449-1477, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01179853

T. N. Lal, M. Schr, N. J. Hill, H. Preissl, M. Bogdan et al., A Brain Computer Interface with Online Feedback based on Magnetoencephalography, 22Nd International Conference on Machine Learning, p.465, 2005.

G. Lioi, S. Butet, M. Fleury, E. Bannier, A. Lecuyer et al., A multi-target motor imagery training using bimodal EEG-fMRI Neurofeedback: a pilot study on chronic stroke patients -in review, Frontiers in Human Neuroscience, vol.14, pp.1-13, 2020.

G. Lioi, C. Cury, L. Perronnet, M. Mano, E. Bannier et al., Simultaneous MRI-EEG during a motor imagery neurofeedback task: an open access brain imaging dataset for multi-modal data integration, 2019.

F. Lotte, F. Larrue, and C. Mühl, Flaws in current human training protocols for spontaneous Brain-Computer Interfaces: lessons learned from instructional design, Frontiers in Human Neuroscience, vol.7, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00862716

M. Lotze and U. Halsband, Motor imagery, Journal of Physiology Paris, vol.99, pp.386-395, 2006.
URL : https://hal.archives-ouvertes.fr/hal-01857445

D. Mantini, M. G. Perrucci, S. Cugini, A. Ferretti, G. L. Romani et al., Complete artifact removal for EEG recorded during continuous fMRI using independent component analysis, NeuroImage, vol.34, pp.598-607, 2007.

C. Maumet, From group to patient-specific analysis of brain function in arterial spin labelling and BOLD functional MRI, 2013.
URL : https://hal.archives-ouvertes.fr/tel-00863908

A. Mayeli, V. Zotev, H. Refai, and J. Bodurka, Real-time EEG artifact correction during fMRI using ICA, Journal of Neuroscience Methods, vol.274, pp.27-37, 2016.

K. Mcinnes, C. Friesen, and S. Boe, Specific brain lesions impair explicit motor imagery ability: A systematic review of the evidence, Archives of Physical Medicine and Rehabilitation, vol.97, pp.478-489, 2016.

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.7, 2012.

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.36, pp.391-414, 2015.

C. Neuper, R. Scherer, M. Reiner, and G. Pfurtscheller, Imagery of motor actions: Differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG, Cognitive Brain Research, vol.25, pp.668-677, 2005.

R. K. Niazy, C. F. Beckmann, G. D. Iannetti, J. M. Brady, and S. M. Smith, Removal of FMRI environment artifacts from EEG data using optimal basis sets, NeuroImage, vol.28, pp.720-737, 2005.

T. Nierhaus, C. Gundlach, D. Goltz, S. D. Thiel, B. Pleger et al., Internal ventilation system of MR scanners induces specific EEG artifact during simultaneous EEG-fMRI, NeuroImage, vol.74, pp.70-76, 2013.

M. Ninaus, S. E. Kober, M. Witte, K. Koschutnig, M. Stangl et al., Neural substrates of cognitive control under the belief of getting neurofeedback training, Frontiers in human neuroscience, vol.7, p.914, 2013.

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, Electroencephalography and Clinical Neurophysiology, vol.103, pp.499-515, 1997.

T. Ono, K. Shindo, K. Kawashima, N. Ota, M. Ito et al., Brain-computer interface with somatosensory feedback improves functional recovery from severe hemiplegia due to chronic stroke, Frontiers in Neuroengineering, vol.7, p.19, 2014.

L. Perronnet, A. Lécuyer, F. Lotte, M. Clerc, and C. Barillot, Brain Training with Neurofeedback, Brain-Computer Interfaces 1: Foundations and Methods, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01413424

L. Bougrain and F. Lotte, , pp.271-292

L. Perronnet, A. Lécuyer, M. Mano, E. Bannier, F. Lotte et al., Unimodal Versus Bimodal EEG-fMRI Neurofeedback of a Motor Imagery Task, Frontiers in Human Neuroscience, vol.11, p.193, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01519755

G. Pfurtscheller and F. Lopes-da-silva, Event-related EEG/MEG synchronization and desynchronization: Basic principles, Clinical Neurophysiology, vol.110, pp.141-149, 1999.

L. Pillette, Redefining and Adapting Feedback for Mental-Imagery based Brain-Computer Interface User Training to the Learners' Traits and States. phdthesis, 2019.
URL : https://hal.archives-ouvertes.fr/tel-02460549

J. D. Power, A. Mitra, T. O. Laumann, A. Z. Snyder, B. L. Schlaggar et al., Comparison of fMRI motion correction software tools, NeuroImage, vol.84, pp.529-543, 2014.

E. Raffin, J. Mattout, K. T. Reilly, and P. Giraux, Disentangling motor execution from motor imagery with the phantom limb, Brain, vol.135, pp.582-595, 2012.

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, pp.441-446, 2000.

P. Ritter, M. Moosmann, and A. Villringer, Rolandic alpha and beta EEG rhythms' strengths are inversely related to fMRI-BOLD signal in primary somatosensory and motor cortex, Human Brain Mapping, vol.30, pp.1168-1187, 2009.

T. Ros, S. Enriquez-geppert, V. Zotev, K. Young, G. Wood et al., Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies, 2019.

P. Sepulveda, R. Sitaram, M. Rana, C. Montalba, C. Tejos et al., How feedback, motor imagery, and reward influence brain self-regulation using real-time fMRI, Human Brain Mapping, vol.37, pp.3153-3171, 2016.

N. Sharma and J. Baron, Does motor imagery share neural networks with executed movement: a multivariate fMRI analysis, Frontiers in Human Neuroscience, vol.7, p.564, 2013.

A. Sirigu, J. Duhamel, L. Cohen, B. Pillon, B. Dubois et al., The Mental Representation of Hand Movements After Parietal Cortex Damage, Science, vol.273, pp.1564-1568, 1996.

R. Sitaram, T. Ros, L. Stoeckel, S. Haller, F. Scharnowski et al., Closedloop brain training: the science of neurofeedback, Nature Reviews Neuroscience, vol.18, pp.86-100, 2016.

T. Sollfrank, A. Ramsay, S. Perdikis, J. Williamson, R. Murray-smith et al., The effect of multimodal and enriched feedback on SMR-BCI performance, Clinical Neurophysiology, vol.127, pp.490-498, 2016.

A. Solodkin, P. Hlustik, E. E. Chen, and S. L. Small, Fine modulation in network activation during motor execution and motor imagery, Cerebral Cortex, vol.14, pp.1246-1255, 2004.

B. Sorger, T. Kamp, N. Weiskopf, J. C. Peters, and R. Goebel, When the brain takes 'BOLD' steps: Real-time fMRI neurofeedback can further enhance the ability to gradually self-regulate regional brain activation, Neuroscience, 2016.

B. Sorger, F. Scharnowski, D. E. Linden, M. Hampson, Y. et al., Control freaks: Towards optimal selection of control conditions for fmri neurofeedback studies, Neuroimage, vol.186, pp.256-265, 2019.

D. Steyrl, G. Krausz, K. Koschutnig, G. Edlinger, and G. R. Müller-putz, Reference layer adaptive filtering (RLAF) for EEG artifact reduction in simultaneous EEG-fMRI, Journal of Neural Engineering, vol.14, p.26003, 2017.

L. E. Stoeckel, K. A. Garrison, S. Ghosh, P. Wighton, C. A. Hanlon et al., , 2014.

, Optimizing real time fMRI neurofeedback for therapeutic discovery and development, NeuroImage: Clinical, vol.5, pp.245-255

G. Sudre, L. Parkkonen, E. Bock, S. Baillet, W. Wang et al., RtMEG: A real-time software interface for magnetoencephalography, Computational Intelligence and Neuroscience, 2011.

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.

J. Sweller, J. J. Van-merrienboer, and F. G. Paas, Cognitive Architecture and Instructional Design, Educational Psychology Review, vol.10, pp.251-296, 1998.

R. T. Thibault, M. Lifshitz, N. Birbaumer, and A. Raz, Neurofeedback, self-regulation, and brain imaging: Clinical science and fad in the service of mental disorders, Psychotherapy and Psychosomatics, vol.84, pp.193-207, 2015.

R. T. Thibault, M. Lifshitz, and A. Raz, The self-regulating brain and neurofeedback: Experimental science and clinical promise, Cortex, vol.74, pp.247-261, 2016.

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 postprocessing EEG/fMRI corrections-A validation of a real-time simultaneous EEG/fMRI correction method, NeuroImage, vol.125, pp.880-894, 2016.

J. D. Wander, T. Blakely, K. J. Miller, K. E. Weaver, L. A. Johnson et al., , 2013.

, Distributed cortical adaptation during learning of a brain-computer interface task, Proceedings of the National Academy of Sciences, vol.110, pp.10818-10823

J. Wang, Y. Yang, L. Fan, J. Xu, C. Li et al., Convergent functional architecture of the superior parietal lobule unraveled with multimodal neuroimaging approaches, Human Brain Mapping, vol.36, pp.238-257, 2015.

C. K. Wong, V. Zotev, M. Misaki, R. Phillips, Q. Luo et al., Automatic EEGassisted retrospective motion correction for fMRI (aE-REMCOR), NeuroImage, vol.129, pp.133-147, 2016.

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.

A. D. Zaidi, M. H. Munk, A. Schmidt, C. Risueno-segovia, R. Bernard et al., , 2015.

, Simultaneous epidural functional near-infrared spectroscopy and cortical electrophysiology as a tool for studying local neurovascular coupling in primates, NeuroImage, vol.120, pp.394-399

C. Zich, S. Debener, C. Kranczioch, M. G. Bleichner, I. Gutberlet et al., Realtime EEG feedback during simultaneous EEG-fMRI identifies the cortical signature of motor imagery, NeuroImage, vol.114, pp.438-447, 2015.

V. Zotev, R. Phillips, H. Yuan, M. Misaki, and J. Bodurka, Self-regulation of human brain activity using simultaneous real-time fMRI and EEG neurofeedback, NeuroImage, vol.85, pp.985-995, 2014.