L. E. Arnold, N. Lofthouse, S. Hersch, X. Pan, E. Hurt et al., EEG Neurofeedback for ADHD, Journal of Attention Disorders, vol.40, issue.5, pp.410-419, 2012.
DOI : 10.1097/00004583-200102000-00011

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

B. Blankertz, Gentle Introduction to Signal Processing and Classification for Single-Trial EEG Analysis, Book: Brain-Computer Interfaces Handbook ? Technological and Theoretical Advances, 2018.

B. Blankertz, S. Lemm, M. Treder, S. Haufe, and K. R. Müller, Single-trial analysis and classification of ERP components ??? A tutorial, NeuroImage, vol.56, issue.2, p.56, 2011.
DOI : 10.1016/j.neuroimage.2010.06.048

S. Brandl, L. Frølich, J. Höhne, K. R. Müller, and W. Samek, Brain???computer interfacing under distraction: an evaluation study, Journal of Neural Engineering, vol.13, issue.5, p.56012, 2016.
DOI : 10.1088/1741-2560/13/5/056012

N. Braun, R. Emkes, J. D. Thorne, and S. Debener, Embodied neurofeedback with an anthropomorphic robotic hand, Sci Rep, p.37696, 2016.

T. Chau and S. Damouras, Reply to ???On the risk of extracting relevant information from random data???, Journal of Neural Engineering, vol.6, issue.5, 2009.
DOI : 10.1088/1741-2560/6/5/058002

S. Chevallier, Riemannian Classification for SSVEP Based BCI: Offline Versus Online Implementations, Book: Brain-Computer Interfaces Handbook ? Technological and Theoretical Advances, 2018.

M. Chiew, S. M. Laconte, and S. J. Graham, Investigation of fMRI neurofeedback of differential primary motor cortex activity using kinesthetic motor imagery, NeuroImage, vol.61, issue.1, pp.61-82, 2012.
DOI : 10.1016/j.neuroimage.2012.02.053

E. Combrisson and K. Jerbi, Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy, Journal of Neuroscience Methods, vol.250, pp.126-136, 2015.
DOI : 10.1016/j.jneumeth.2015.01.010

J. Daunizeau, K. Preuschoff, K. Friston, and K. Stephan, Optimizing Experimental Design for Comparing Models of Brain Function, PLoS Computational Biology, vol.21, issue.11, p.1002280, 2011.
DOI : 10.1371/journal.pcbi.1002280.s003

S. Debener, R. Emkes, M. De-vos, and M. Bleichner, Unobtrusive ambulatory EEG using a smartphone and flexible printed electrodes around the ear, Scientific Reports, vol.3, issue.1, p.16743, 2015.
DOI : 10.1088/1741-2560/3/1/R02

D. Vos, M. Kroesen, M. Emkes, R. Debener, and S. , P300 speller BCI with a mobile EEG system: comparison to a traditional amplifier, Journal of Neural Engineering, vol.11, issue.3, p.36008, 2014.
DOI : 10.1088/1741-2560/11/3/036008

F. Dolcos, K. S. Labar, and R. Cabeza, Dissociable effects of arousal and valence on prefrontal activity indexing emotional evaluation and subsequent memory: an event-related fMRI study, NeuroImage, vol.23, issue.1, pp.64-74, 2004.
DOI : 10.1016/j.neuroimage.2004.05.015

L. G. Dominguez, On the risk of extracting relevant information from random data, Journal of Neural Engineering, vol.6, issue.5, p.58001, 2009.
DOI : 10.1088/1741-2560/6/5/058001

H. J. Engelbregt, D. Keeser, L. Van-eijk, E. M. Suiker, D. Eichhorn et al., Short and long-term effects of sham-controlled prefrontal EEG-neurofeedback training in healthy subjects, Clinical Neurophysiology, vol.127, issue.4, pp.1931-1937, 2016.
DOI : 10.1016/j.clinph.2016.01.004

C. Escolano, M. Navarro-gil, J. Garcia-campayo, and J. Minguez, The Effects of a Single Session of Upper Alpha Neurofeedback for Cognitive Enhancement: A Sham-Controlled Study, Applied Psychophysiology and Biofeedback, vol.54, issue.2, pp.227-236
DOI : 10.1016/j.neuroimage.2010.08.078

L. Farwell and E. Donchin, Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials, Electroencephalography and Clinical Neurophysiology, vol.70, issue.6, pp.510-523, 1988.
DOI : 10.1016/0013-4694(88)90149-6

M. Fatourechi, A. Bashashati, R. Ward, and G. Birch, EMG and EOG artifacts in brain computer interface systems: A survey, Clinical Neurophysiology, vol.118, issue.3, pp.480-494, 2007.
DOI : 10.1016/j.clinph.2006.10.019

J. Frey, R. Gervais, S. Fleck, F. Lotte, and M. Hachet, Teegi, Proceedings of the 27th annual ACM symposium on User interface software and technology, UIST '14, pp.301-308, 2014.
DOI : 10.1145/2642918.2647368

URL : https://hal.archives-ouvertes.fr/hal-01025621

H. Gevensleben, B. Albrecht, H. Lütcke, T. Auer, W. I. Dewiputri et al., Neurofeedback of slow cortical potentials: neural mechanisms and feasibility of a placebo-controlled design in healthy adults, Frontiers in Human Neuroscience, vol.12, p.990, 2014.
DOI : 10.1038/jcbfm.1992.127

A. Gharabaghi, D. Kraus, M. T. Leao, M. Spüler, A. Walter et al., Coupling brain-machine interfaces with cortical stimulation for brain-state dependent stimulation: enhancing motor cortex excitability for neurorehabilitation, Frontiers in Human Neuroscience, vol.113, issue.87, 2014.
DOI : 10.1016/S1388-2457(02)00057-3

I. Goncharova, D. Mcfarland, T. Vaughan, and J. Wolpaw, EMG contamination of EEG: spectral and topographical characteristics, Clinical Neurophysiology, vol.114, issue.9, pp.1580-1593, 2003.
DOI : 10.1016/S1388-2457(03)00093-2

T. Harmelech, D. Friedman, and R. Malach, Differential Magnetic Resonance Neurofeedback Modulations across Extrinsic (Visual) and Intrinsic (Default-Mode) Nodes of the Human Cortex, Journal of Neuroscience, vol.35, issue.6, pp.2588-2595, 2015.
DOI : 10.1523/JNEUROSCI.3098-14.2015

D. Heingartner, Mental block IEEE Spectrum, pp.42-43, 2009.

R. Henson, Efficient experimental design for fMRI Statistical Parametric Mapping: The Analysis of Functional Brain Images, pp.193-210, 2006.

M. K. Islam, A. Rastegarnia, and Z. Yang, Methods for artifact detection and removal from scalp EEG: A review, Neurophysiologie Clinique/Clinical Neurophysiology, vol.46, issue.4-5, pp.287-305, 2016.
DOI : 10.1016/j.neucli.2016.07.002

K. C. Kadosh, Q. Luo, C. De-burca, M. O. Sokunbi, J. Feng et al., Using realtime fMRI to influence effective connectivity in the developing emotion regulation network NeuroImage, pp.15-616, 2016.

S. C. Kleih and A. Kübler, Why User-Centered Design is Relevant for Brain-Computer Interfacing and How It can be Implemented in Study Protocols, Book: Brain-Computer Interfaces Handbook ? Technological and Theoretical Advances, 2018.

R. Kobler and R. Scherer, Restricted Boltzmann Machines in Sensory Motor Rhythm Brain-Computer Interfacing: A study on inter-subject transfer and co-adaptation, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2016.
DOI : 10.1109/SMC.2016.7844284

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, pp.21-30, 2014.
DOI : 10.1016/j.biopsycho.2013.05.005

A. Kübler, D. Mattia, R. Rupp, and M. Tangermann, Facing the challenge: Bringing braincomputer interfaces to end-users, In: Artificial intelligence in medicine, 2013.

L. Vaque, T. J. Rossiter, and T. , The Ethical Use of Placebo Controls in Clinical Research: The Declaration of Helsinki, Applied Psychophysiology and Biofeedback, vol.26, issue.1, pp.23-37, 2001.
DOI : 10.1023/A:1009563504319

E. C. Lalor, S. P. Kelly, C. Finucane, R. Burke, R. Smith et al., Steadystate VEP-based brain-computer interface control in an immersive 3D gaming environment, EURASIP journal on applied signal processing, pp.3156-3164, 2005.

A. Lécuyer, F. Lotte, R. Reilly, R. Leeb, M. Hirose et al., Brain-Computer Interfaces, Virtual Reality, and Videogames, Computer, vol.41, issue.10, pp.66-72, 2008.
DOI : 10.1109/MC.2008.410

J. H. Lee, J. Kim, and S. S. Yoo, Real-time fMRI-based neurofeedback reinforces causality of attention networks, Neuroscience Research, vol.72, issue.4, pp.347-354, 2012.
DOI : 10.1016/j.neures.2012.01.002

S. Lemm, B. Blankertz, T. Dickhaus, and K. R. Müller, Introduction to machine learning for brain imaging, NeuroImage, vol.56, issue.2, pp.387-399, 2011.
DOI : 10.1016/j.neuroimage.2010.11.004

V. Litvak, J. Mattout, S. Kiebel, C. Phillips, R. Henson et al., EEG and MEG Data Analysis in SPM8, Computational Intelligence and Neuroscience, vol.19, issue.2, 2011.
DOI : 10.1073/pnas.98.2.694

F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, and B. Arnaldi, A review of classification algorithms for EEG-based brain???computer interfaces, Journal of Neural Engineering, vol.4, issue.2, p.1, 2007.
DOI : 10.1088/1741-2560/4/2/R01

URL : https://hal.archives-ouvertes.fr/inria-00134950

F. Lotte, Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain–Computer Interfaces, Proceedings of the IEEE, pp.871-890, 2015.
DOI : 10.1109/JPROC.2015.2404941

S. Luu and T. Chau, Decoding subjective preference from single-trial near-infrared spectroscopy signals, Journal of Neural Engineering, vol.6, issue.1, 2008.
DOI : 10.1088/1741-2560/6/1/016003

E. Maris and R. Oostenveld, Nonparametric statistical testing of EEG- and MEG-data, Journal of Neuroscience Methods, vol.164, issue.1, pp.177-190, 2007.
DOI : 10.1016/j.jneumeth.2007.03.024

D. J. Mcfarland, Therapeutic Applications of BCI Technologies. Book: Brain-Computer Interfaces Handbook ? Technological and Theoretical Advances, 2018.

F. Melinscak and L. Montesano, Beyond p -values in the evaluation of brain???computer interfaces: A Bayesian estimation approach, Journal of Neuroscience Methods, vol.270, pp.30-45, 2016.
DOI : 10.1016/j.jneumeth.2016.06.008

M. Mihara, I. Miyai, N. Harrori, 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, p.32234, 2012.
DOI : 10.1371/journal.pone.0032234.t004

M. Mihara, N. Hattori, M. Hatakenaka, H. Yagura, T. Kawano et al., 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

J. D. Millán, R. Rupp, G. Mueller-putz, R. Murray-smith, C. Giugliemma et al., Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges, Frontiers in Neuroscience, vol.1, p.161, 2010.
DOI : 10.3389/fnins.2010.00161

J. Mladenovic, J. Mattout, and F. Lotte, A Generic Framework for Adaptive EEG-Based BCI Training and Operation, Book: Brain-Computer Interfaces Handbook ? Technological and Theoretical Advances, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01542504

C. Mühl, P. D. Bos, M. E. Thurlings, L. Scherffig, M. Duvinage et al., Bacteria Hunt, Workshop Report for the Interface Workshop, pp.1-22, 2009.
DOI : 10.1177/155005940904000311

C. Mühl, C. Jeunet, and F. Lotte, EEG-based Workload Estimation Across Affective Contexts Frontiers in Neuroscience section Neuroprosthetics, p.114, 2014.

C. Mühl, B. Allison, A. Nijholt, and G. Chanel, A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges, Brain-Computer Interfaces, vol.14, issue.2, pp.1-19, 2014.
DOI : 10.1109/ACII.2013.102

G. R. Mu?-ller-putz, R. Scherer, C. Brauneis, and G. Pfurtscheller, Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components, Journal of Neural Engineering, vol.2, issue.4, pp.123-153, 2005.
DOI : 10.1088/1741-2560/2/4/008

G. R. Mu?-ller-putz, R. Scherer, C. Neuper, and G. Pfurtscheller, Steady-State Somatosensory Evoked Potentials: Suitable Brain Signals for Brain???Computer Interfaces?, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.14, issue.1, pp.30-37, 2006.
DOI : 10.1109/TNSRE.2005.863842

G. Müller-putz, R. Scherer, C. Brunner, R. Leeb, and G. Pfurtscheller, Better than random: a closer look on BCI results, International Journal of Bioelectromagnetism, vol.10, pp.52-55, 2008.

T. Nichols, False Discovery Rate procedures Statistical Parametric Mapping: The Analysis of Functional Brain Images, pp.246-252, 2006.

Y. O. Okazaki, J. M. Horschig, L. Luther, R. Oostenveld, I. Murakami et al., Real-time MEG neurofeedback training of posterior alpha activity modulates subsequent visual detection performance, NeuroImage, vol.107, pp.323-332, 2015.
DOI : 10.1016/j.neuroimage.2014.12.014

E. Olivetti, A. Mognon, S. Greiner, and P. Avesani, Brain Decoding: Biases in Error Estimation, 2010 First Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging, pp.40-43, 2010.
DOI : 10.1109/WBD.2010.9

R. Oostenveld, P. Fries, E. Maris, and J. Schoffelen, FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data, Computational Intelligence and Neuroscience, vol.36, issue.3, 2011.
DOI : 10.1016/j.neuroimage.2009.02.041

G. Pfurtscheller, C. Neuper, D. Flotzinger, and M. Pregenzer, EEG-based discrimination between imagination of right and left hand movement, Electroencephalography and Clinical Neurophysiology, vol.103, issue.6, pp.642-651, 1997.
DOI : 10.1016/S0013-4694(97)00080-1

T. Ros, J. Theberge, P. A. Frewen, R. Kluetsch, M. Densmore et al., Mind over chatter: Plastic up-regulation of the fMRI salience network directly after EEG neurofeedback, NeuroImage, vol.65, pp.324-335, 2013.
DOI : 10.1016/j.neuroimage.2012.09.046

G. Sanchez, F. Lecaignard, A. Otman, E. Maby, and J. Mattout, Active SAmpling Protocol (ASAP) to Optimize Individual Neurocognitive Hypothesis Testing: A BCI-Inspired Dynamic Experimental Design, Frontiers in Human Neuroscience, vol.27, issue.114, 2016.
DOI : 10.1002/sim.3381

L. A. Schmidt and L. J. Trainor, Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions, Cognition & Emotion, vol.9, issue.4, pp.487-500, 2001.
DOI : 10.1162/jocn.1996.8.1.29

E. Singer, Brain games, Technology Review, vol.111, issue.4, pp.82-84, 2008.

D. Steyrl, R. Scherer, J. Faller, and G. R. Müller-putz, Abstract, Biomedical Engineering / Biomedizinische Technik, vol.20, issue.1, pp.61-77, 2016.
DOI : 10.1109/34.709601

D. Vernon, A. Frick, and J. Gruzelier, Neurofeedback as a Treatment for ADHD: A Methodological Review with Implications for Future Research, Journal of Neurotherapy, vol.8, issue.2, pp.53-82, 2004.
DOI : 10.1300/J184v08n02_04

E. M. Whitham, K. J. Pope, S. P. Fitzgibbon, T. Lewis, C. R. Clark et al., Scalp electrical recording during paralysis: Quantitative evidence that EEG frequencies above 20Hz are contaminated by EMG, Clinical Neurophysiology, vol.118, issue.8, pp.1877-1888, 2007.
DOI : 10.1016/j.clinph.2007.04.027

M. Witte, S. E. Kober, M. Ninaus, C. Neuper, and G. Wood, Control beliefs can predict the ability to up-regulate sensorimotor rhythm during neurofeedback training, Frontiers in Human Neuroscience, vol.7, p.478, 2013.
DOI : 10.3389/fnhum.2013.00478

K. Worsley, Random Field Theory Statistical Parametric Mapping: The Analysis of Functional Brain Images, pp.232-236, 2006.

S. Yao, B. Becker, Y. Geng, Z. Zhao, X. Xu et al., Voluntary control of anterior insula and its functional connections is feedback-independent and increases pain empathy, NeuroImage, vol.130, pp.230-240, 2016.
DOI : 10.1016/j.neuroimage.2016.02.035

T. O. Zander and C. Kothe, Towards passive brain???computer interfaces: applying brain???computer interface technology to human???machine systems in general, Journal of Neural Engineering, vol.8, issue.2, p.25005, 2011.
DOI : 10.1088/1741-2560/8/2/025005

C. Zich, S. Debener, D. Vos, M. Frerichs, S. Maurer et al., Lateralization patterns of covert but not overt movements change with age: An EEG neurofeedback study, NeuroImage, vol.116, issue.116, pp.1-80, 2015.
DOI : 10.1016/j.neuroimage.2015.05.009

V. Zotev, H. Yuan, M. Misaki, R. Phillips, K. D. Young et al., Correlation between amygdala BOLD activity and frontal EEG asymmetry during real-time fMRI neurofeedback training in patients with depression, NeuroImage: Clinical, vol.11, pp.224-262, 2016.
DOI : 10.1016/j.nicl.2016.02.003