R. Näätä-nen, P. Astikainen, T. Ruusuvirta, and M. Huotilainen, Automatic auditory intelligence: An expression of the sensory???cognitive core of cognitive processes, Brain Research Reviews, vol.64, issue.1, pp.123-136, 2010.
DOI : 10.1016/j.brainresrev.2010.03.001

A. Goldstein, K. Spencer, and E. Donchin, The influence of stimulus deviance and novelty on the P300 and Novelty P3, Psychophysiology, vol.39, issue.6, pp.781-790, 2002.
DOI : 10.1111/1469-8986.3960781

R. Hari, M. Hämälä-inen, R. Ilmoniemi, E. Kaukoranta, and K. Reinikainen, Responses of the primary auditory cortex to pitch changes in a sequence of tone pips: Neuromagnetic recordings in man, Neuroscience Letters, vol.50, issue.1-3, pp.127-132, 1984.
DOI : 10.1016/0304-3940(84)90474-9

M. Garrido, J. Kilner, K. Stephan, and K. Friston, The mismatch negativity: A review of underlying mechanisms, Clinical Neurophysiology, vol.120, issue.3, pp.453-463, 2009.
DOI : 10.1016/j.clinph.2008.11.029

R. Näätä-nen, P. Paavilainen, T. Rinne, and K. Alho, The mismatch negativity (MMN) in basic research of central auditory processing: a review, Clinical Neurophysiology, vol.118, 2007.

E. Halgren, P. Baudena, J. Clarke, G. Heit, and K. Marinkovic, Intracerebral potentials to rare target and distractor auditory and visual stimuli. II. Medial, lateral and posterior temporal lobe, Electroencephalography and Clinical Neurophysiology, vol.94, issue.4, pp.229-250, 1995.
DOI : 10.1016/0013-4694(95)98475-N

T. Bekinschtein, S. Dehaene, B. Rohaut, F. Tadel, and L. Cohen, Neural signature of the conscious processing of auditory regularities, Proceedings of the National Academy of Sciences, vol.106, issue.5, pp.1672-1677, 2009.
DOI : 10.1073/pnas.0809667106

J. Polich, Updating P300: An integrative theory of P3a and P3b, Clinical Neurophysiology, vol.118, issue.10, pp.2128-2148, 2007.
DOI : 10.1016/j.clinph.2007.04.019

F. Faugeras, B. Rohaut, N. Weiss, T. Bekinschtein, and D. Galanaud, Probing consciousness with event-related potentials in the vegetative state, Neurology, vol.77, issue.3, pp.264-268, 2011.
DOI : 10.1212/WNL.0b013e3182217ee8
URL : https://hal.archives-ouvertes.fr/hal-00795745

F. Faugeras, B. Rohaut, N. Weiss, T. Bekinschtein, and D. Galanaud, Event related potentials elicited by violations of auditory regularities in patients with impaired consciousness, Neuropsychologia, vol.50, issue.3, pp.403-418, 2012.
DOI : 10.1016/j.neuropsychologia.2011.12.015
URL : https://hal.archives-ouvertes.fr/hal-00795705

J. King, F. Faugeras, A. Gramfort, A. Schurger, and I. Karoui, Single-trial decoding of auditory novelty responses facilitates the detection of residual consciousness, NeuroImage, vol.83, pp.726-738, 2013.
DOI : 10.1016/j.neuroimage.2013.07.013

S. Chennu, V. Noreika, D. Gueorguiev, A. Blenkmann, and S. Kochen, Expectation and Attention in Hierarchical Auditory Prediction, Journal of Neuroscience, vol.33, issue.27, pp.11194-11205, 2013.
DOI : 10.1523/JNEUROSCI.0114-13.2013

C. Fischer, J. Luaute, and D. Morlet, Event-related potentials (MMN and novelty P3) in permanent vegetative or minimally conscious states, Clinical Neurophysiology, vol.121, issue.7, pp.1032-1042, 2010.
DOI : 10.1016/j.clinph.2010.02.005

H. Tiitinen, P. May, K. Reinikainen, and R. Näätänen, Attentive novelty detection in humans is governed by pre-attentive sensory memory, Nature, vol.372, issue.6501, pp.90-92, 1994.
DOI : 10.1038/372090a0

C. Wacongne, E. Labyt, V. Van-wassenhove, T. Bekinschtein, and L. Naccache, Evidence for a hierarchy of predictions and prediction errors in human cortex, Proceedings of the National Academy of Sciences, vol.108, issue.51, pp.20754-20759, 2011.
DOI : 10.1073/pnas.1117807108

J. Polich and C. Margala, P300 and probability: comparison of oddball and single-stimulus paradigms, International Journal of Psychophysiology, vol.25, issue.2, pp.169-176, 1997.
DOI : 10.1016/S0167-8760(96)00742-8

N. Squires, K. Squires, and S. Hillyard, Two varieties of long-latency positive waves evoked by unpredictable auditory stimuli in man, Electroencephalography and Clinical Neurophysiology, vol.38, issue.4, pp.387-40110, 1975.
DOI : 10.1016/0013-4694(75)90263-1

S. Dehaene, J. Changeux, L. Naccache, J. Sackur, and C. Sergent, Conscious, preconscious, and subliminal processing: a testable taxonomy, Trends in Cognitive Sciences, vol.10, issue.5, pp.204-211, 2006.
DOI : 10.1016/j.tics.2006.03.007
URL : https://hal.archives-ouvertes.fr/pasteur-00161465

M. Garrido, J. Kilner, S. Kiebel, K. Stephan, and K. Friston, Dynamic causal modelling of evoked potentials: A reproducibility study, NeuroImage, vol.36, issue.3, pp.571-580, 2007.
DOI : 10.1016/j.neuroimage.2007.03.014

C. Wacongne, J. Changeux, and S. Dehaene, A Neuronal Model of Predictive Coding Accounting for the Mismatch Negativity, Journal of Neuroscience, vol.32, issue.11, pp.3665-3678, 2012.
DOI : 10.1523/JNEUROSCI.5003-11.2012
URL : https://hal.archives-ouvertes.fr/cea-00842907

R. Rao and D. Ballard, Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects, Nature Neuroscience, vol.2, issue.1, pp.79-87, 1999.
DOI : 10.1038/4580

A. Pouget, S. Deneve, and J. Duhamel, A computational perspective on the neural basis of multisensory spatial representations, Nature Reviews Neuroscience, vol.83, issue.9, pp.741-747, 2002.
DOI : 10.1038/nrn914

K. Friston, A theory of cortical responses, Philosophical Transactions of the Royal Society B: Biological Sciences, vol.335, issue.6188, pp.815-836, 2005.
DOI : 10.1038/335311a0

S. Taulu, M. Kajola, and J. Simola, Suppression of Interference and Artifacts by the Signal Space Separation Method, Brain Topography, vol.16, issue.4, pp.269-275, 2004.
DOI : 10.1023/B:BRAT.0000032864.93890.f9

M. Uusitalo and R. Ilmoniemi, Signal-space projection method for separating MEG or EEG into components, Medical & Biological Engineering & Computing, vol.7, issue.Suppl. A, pp.135-140, 1997.
DOI : 10.1007/BF02534144

S. Lemm, B. Blankertz, T. Dickhaus, and K. 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

A. Gramfort, M. Luessi, E. Larson, D. Engemann, and D. Strohmeier, MNE software for processing MEG and EEG data Available: http://martinos, 2013.

A. Gramfort, M. Luessi, E. Larson, D. Engemann, and D. Strohmeier, MNE software for processing MEG and EEG data, NeuroImage, vol.86, pp.1-33, 2013.
DOI : 10.1016/j.neuroimage.2013.10.027

D. Wakeman and R. Henson, Functional and structural connectivity in face-processing: MEG, EEG, fMRI, MRI and DWI data, 2010.

C. Chang and C. Lin, LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, pp.1-30, 2001.
DOI : 10.1145/1961189.1961199

C. Tallon-baudry and O. Bertrand, Oscillatory gamma activity in humans and its role in object representation, Trends in Cognitive Sciences, vol.3, issue.4, pp.151-16210, 1999.
DOI : 10.1016/S1364-6613(99)01299-1

J. Platt, Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods, Advances in Large Margin Classifiers, vol.10, pp.61-74, 1999.

F. Pedregosa, R. Weiss, and M. Brucher, Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

M. Lindquist, B. Caffo, and C. Crainiceanu, Ironing out the statistical wrinkles in ???ten ironic rules???, NeuroImage, vol.81, pp.499-502, 2013.
DOI : 10.1016/j.neuroimage.2013.02.056

K. Friston, Sample size and the fallacies of classical inference, NeuroImage, vol.81, pp.503-504, 2013.
DOI : 10.1016/j.neuroimage.2013.02.057

M. Ingre, Why small low-powered studies are worse than large high-powered studies and how to protect against ???trivial??? findings in research: Comment on Friston (2012), NeuroImage, vol.81, pp.496-498, 2013.
DOI : 10.1016/j.neuroimage.2013.03.030

. Nikolic´dnikolic´nikolic´d, S. Häusler, W. Singer, and W. Maass, Distributed fading memory for stimulus properties in the primary visual cortex, PLoS biology, vol.7, 2009.

M. Stokes, M. Kusunoki, N. Sigala, H. Nili, and D. Gaffan, Dynamic Coding for Cognitive Control in Prefrontal Cortex, Neuron, vol.78, issue.2, pp.364-375, 2013.
DOI : 10.1016/j.neuron.2013.01.039

E. Meyers, D. Freedman, G. Kreiman, E. Miller, and T. Poggio, Dynamic Population Coding of Category Information in Inferior Temporal and Prefrontal Cortex, Journal of Neurophysiology, vol.100, issue.3, pp.1407-1419, 2008.
DOI : 10.1152/jn.90248.2008

M. Philiastides and P. Sajda, Temporal Characterization of the Neural Correlates of Perceptual Decision Making in the Human Brain, Cerebral Cortex, vol.16, issue.4, 2006.
DOI : 10.1093/cercor/bhi130

Y. Zhang, E. Meyers, N. Bichot, T. Serre, and T. Poggio, Object decoding with attention in inferior temporal cortex, Proceedings of the National Academy of Sciences, vol.108, issue.21, pp.8850-8855, 2011.
DOI : 10.1073/pnas.1100999108

T. Carlson, High temporal resolution decoding of object position and category, Journal of Vision, vol.11, issue.10, 2011.
DOI : 10.1167/11.10.9

T. Carlson, D. Tovar, and N. Kriegeskorte, Representational dynamics of object vision: The first 1000 ms, Journal of Vision, vol.13, issue.10, pp.1-19, 2013.
DOI : 10.1167/13.10.1

L. Fuentemilla, W. Penny, N. Cashdollar, N. Bunzeck, and E. Düzel, Theta-Coupled Periodic Replay in Working Memory, Current Biology, vol.20, issue.7, pp.606-612, 2010.
DOI : 10.1016/j.cub.2010.01.057

A. Schurger, S. Marti, and S. Dehaene, Reducing multi-sensor data to a single time course that reveals experimental effects, BMC Neuroscience, vol.14, issue.1, pp.122-132, 2013.
DOI : 10.1016/j.neuroimage.2011.03.017
URL : https://hal.archives-ouvertes.fr/inserm-00914194

K. Duncan, A. Hadjipapas, S. Li, Z. Kourtzi, and A. Bagshaw, Identifying spatially overlapping local cortical networks with MEG, Human Brain Mapping, vol.22, issue.7, pp.1003-1016, 2010.
DOI : 10.1002/hbm.20912

K. Sandberg, B. Bahrami, R. Kanai, G. Barnes, and M. Overgaard, Early Visual Responses Predict Conscious Face Perception within and between Subjects during Binocular Rivalry, Journal of Cognitive Neuroscience, vol.19, issue.6, pp.969-985, 2013.
DOI : 10.1016/j.visres.2007.07.007

P. Ramkumar, M. Jas, S. Pannasch, R. Hari, and L. Parkkonen, Feature-Specific Information Processing Precedes Concerted Activation in Human Visual Cortex, Journal of Neuroscience, vol.33, issue.18, pp.7691-7699, 2013.
DOI : 10.1523/JNEUROSCI.3905-12.2013

J. Garcia, R. Srinivasan, and J. Serences, Near-Real-Time Feature-Selective Modulations in Human Cortex, Current Biology, vol.23, issue.6, pp.515-522, 2013.
DOI : 10.1016/j.cub.2013.02.013

R. Näätä-nen and T. Picton, The N1 Wave of the Human Electric and Magnetic Response to Sound: A Review and an Analysis of the Component Structure, Psychophysiology, vol.14, issue.4, pp.375-425, 1987.
DOI : 10.1016/0013-4694(84)90122-6

M. Garrido, K. Friston, S. Kiebel, K. Stephan, and T. Baldeweg, The functional anatomy of the MMN: A DCM study of the roving paradigm, NeuroImage, vol.42, issue.2, pp.936-944, 2008.
DOI : 10.1016/j.neuroimage.2008.05.018

S. Kojima and P. Goldman-rakic, Delay-related activity of prefrontal neurons in rhesus monkeys performing delayed response, Brain Research, vol.248, issue.1, pp.43-5010, 1982.
DOI : 10.1016/0006-8993(82)91145-3

R. Romo, C. Brody, A. Hernández, and L. Lemus, Neuronal correlates of parametric working memory in the prefrontal cortex, Nature, vol.399, issue.6735, pp.470-473, 1999.
DOI : 10.1038/20939

J. Fuster, The Prefrontal Cortex. Raven, editor Academic Press. doi:10, pp.896-627380673, 2008.

S. Dehaene and J. Changeux, Experimental and Theoretical Approaches to Conscious Processing, Neuron, vol.70, issue.2, pp.200-227, 2011.
DOI : 10.1016/j.neuron.2011.03.018
URL : https://hal.archives-ouvertes.fr/hal-00717254