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, Je reconnais avoir été informé que je peux mettre fin à ma participation au projet à tout moment sans aucun motif à donner, en contactant simplement les personnes contacts référencées ci-dessus

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A. Bendixen, Predictability effects in auditory scene analysis : a review, Frontiers in neuroscience, vol.8, p.60, 2014.

J. Benghozi, Classification des phases de sommeil

K. Blocka, Eeg (electroencephalogram)

L. C. Jeffrey-w-britton, . Frey, P. Hopp, . Korb, . Mz-koubeissi et al., Electroencephalography (EEG) : An introductory text and atlas of normal and abnormal findings in adults, children, and infants, 2016.

S. Butterworth, On the theory of filter amplifiers, Wireless Engineer, vol.7, issue.6, pp.536-541, 1930.

C. E-colin, Some experiments on the recognition of speech, with one and with two ears, The Journal of the acoustical society of America, vol.25, issue.5, pp.975-979, 1953.

M. Clerc, L. Bougrain, and F. Lotte, Methods and Perspectives, vol.1, 2016.

W. James, J. W. Cooley, and . Tukey, An algorithm for the machine calculation of complex fourier series, Mathematics of computation, vol.19, issue.90, pp.297-301, 1965.

S. Deike, L. Susan, E. Denham, and . Sussman, Probing auditory scene analysis, Frontiers in neuroscience, vol.8, p.293, 2014.

S. Deng, R. Srinivasan, T. Lappas, and M. Zmura, Eeg classification of imagined syllable rhythm using hilbert spectrum methods, Journal of neural engineering, vol.7, issue.4, p.46006, 2010.

L. Dolle?al, A. Brechmann, G. M. Klump, and S. Deike, Evaluating auditory stream segregation of sam tone sequences by subjective and objective psychoacoustical tasks, and brain activity, Frontiers in neuroscience, vol.8, p.119, 2014.

M. Grierson, C. Kiefer, and M. Yee-king, Progress report on the eavi bci toolkit for music : Musical applications of algorithms for use with consumer brain computer interfaces, 2011.

C. Guger, G. Krausz, G. Brendan-z-allison, and . Edlinger, Comparison of dry and gel based electrodes for p300 brain-computer interfaces, Frontiers in neuroscience, vol.6, p.60, 2012.

H. Hotelling, Relations between two sets of variates, Breakthroughs in statistics, pp.162-190, 1992.

. Inria, Des balbutiements de l

S. John, A. Dimitrijevic, and T. W. Picton, Auditory steady-state responses to exponential modulation envelopes, Ear and hearing, vol.23, issue.2, pp.106-117, 2002.

. Michael-s-john, G. Otavio, B. L. Lins, T. W. Boucher, and . Picton, Multiple auditory steady-state responses (master) : stimulus and recording parameters, Audiology, vol.37, issue.2, pp.59-82, 1998.

M. Hirohito, I. Kondo, D. Toshima, M. Pressnitzer, and . Kashino, Probing the time course of head-motion cues integration during auditory scene analysis, Frontiers in neuroscience, vol.8, p.170, 2014.

E. Kristensen, Méthodologie de traitement conjoint des signaux EEG et oculométriques : applications aux tâches d'exploration visuelle libre, 2017.

F. Lotte, L. Bougrain, A. Cichocki, M. Clerc, and M. Congedo, Alain Rakotomamonjy, and Florian Yger. A review of classification algorithms for eeg-based brain-computer interfaces : a 10 year update, Journal of neural engineering, vol.15, issue.3, p.31005, 2018.

J. Steven and . Luck, An introduction to the event-related potential technique, 2014.

R. Tim-r-mullen, A. Warp, and . Jansch, Minding the (transatlantic) gap : An internetenabled acoustic brain-computer music interface, NIME, pp.469-472, 2011.

. Muse, A deep dive into brainwaves : Brainwave frequencies explained

C. Noronha, A brief introduction to eeg and the types of electrodes

S. Nozaradan, Exploring how musical rhythm entrains brain activity with electroencephalogram frequency-tagging, Philosophical Transactions of the Royal Society B : Biological Sciences, vol.369, p.20130393, 1658.

S. Nozaradan, I. Peretz, M. Missal, and A. Mouraux, Tagging the neuronal entrainment to beat and meter, Journal of Neuroscience, vol.31, issue.28, pp.10234-10240, 2011.

, Lobes of the brain

. Openvibe and . Openvibe, software for brain computer interfaces and real time neurosciences

A. Pinegger, H. Hiebel, C. Selina, and G. Wriessnegger, Composing only by thought : Novel application of the p300 brain-computer interface, PloS one, vol.12, issue.9, p.181584, 2017.

D. Regan, Human brain electrophysiology. Evoked potentials and evoked magnetic fields in science and medicine, 1989.

S. Rebecca, R. J. Schaefer, P. Vlek, and . Desain, Decomposing rhythm processing : Electroencephalography of perceived and self-imposed rhythmic patterns, Psychological research, vol.75, issue.2, pp.95-106, 2011.

, Comprendre le cerveau et son fonctionnement, Futura Sciences

L. Shuai and M. Elhilali, Task-dependent neural representations of salient events in dynamic auditory scenes, Frontiers in neuroscience, vol.8, p.203, 2014.

R. Srinivasan, S. Thorpe, S. Deng, T. Lappas, and M. Zmura, Decoding attentional orientation from eeg spectra, International Conference on Human-Computer Interaction, pp.176-183, 2009.

J. Sussman, -. Fort, and E. Sussman, The effect of stimulus context on the buildup to stream segregation, Frontiers in neuroscience, vol.8, p.93, 2014.

P. Welch, The use of fast fourier transform for the estimation of power spectra : a method based on time averaging over short, modified periodograms, IEEE Transactions on audio and electroacoustics, vol.15, issue.2, pp.70-73, 1967.