H. Larralde and F. Leyvraz, Metastability for Markov Processes with Detailed Balance, Physical Review Letters, vol.94, issue.16, 2005.
DOI : 10.1103/PhysRevLett.94.160201

P. Deuflhard and M. Weber, Robust Perron cluster analysis in conformation dynamics, Linear Algebra and its Applications, vol.398, pp.161-184, 2005.
DOI : 10.1016/j.laa.2004.10.026

G. Froyland, K. Padberg, M. H. England, and A. M. Treguier, Detection of Coherent Oceanic Structures via Transfer Operators, Physical Review Letters, vol.98, issue.22, 2007.
DOI : 10.1103/PhysRevLett.98.224503

URL : https://hal.archives-ouvertes.fr/hal-00270767

K. J. Friston, Transients, Metastability, and Neuronal Dynamics, NeuroImage, vol.5, issue.2, pp.164-171, 1997.
DOI : 10.1006/nimg.1997.0259

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

M. I. Rabinovich, R. Huerta, and G. Laurent, NEUROSCIENCE: Transient Dynamics for Neural Processing, Science, vol.321, issue.5885, pp.48-50, 2008.
DOI : 10.1126/science.1155564

A. E. Hudson, D. P. Calderon, D. W. Pfaff, and A. Proekt, Recovery of consciousness is mediated by a network of discrete metastable activity states, Proceedings of the National Academy of Sciences of the U.S.A, pp.9283-9288, 2014.
DOI : 10.1073/pnas.1408296111

I. B. Yildiz and S. J. Kiebel, A Hierarchical Neuronal Model for Generation and Online Recognition of Birdsongs, PLoS Computational Biology, vol.3, issue.12, pp.1002303-2011
DOI : 10.1371/journal.pcbi.1002303.s006

O. Oullier and J. A. Kelso, Neuroeconomics and the metastable brain, Trends in Cognitive Sciences, vol.10, issue.8, pp.353-354, 2006.
DOI : 10.1016/j.tics.2006.06.009

URL : https://hal.archives-ouvertes.fr/hal-00095053

E. Tognoli and J. Kelso, The Metastable Brain, Neuron, vol.81, issue.1, pp.35-48, 2014.
DOI : 10.1016/j.neuron.2013.12.022

A. Hutt and H. Riedel, Analysis and modeling of quasi-stationary multivariate time series and their application to middle latency auditory evoked potentials, Physica D: Nonlinear Phenomena, vol.177, issue.1-4, pp.203-232, 2003.
DOI : 10.1016/S0167-2789(02)00747-9

A. Hutt, AN ANALYTICAL FRAMEWORK FOR MODELING EVOKED AND EVENT-RELATED POTENTIALS, International Journal of Bifurcation and Chaos, vol.14, issue.02, pp.653-666, 2004.
DOI : 10.1142/S0218127404009351

O. Mazor and G. Laurent, Transient Dynamics versus Fixed Points in Odor Representations by Locust Antennal Lobe Projection Neurons, Neuron, vol.48, issue.4, pp.661-673, 2005.
DOI : 10.1016/j.neuron.2005.09.032

C. Allefeld, H. Atmanspacher, and J. Wackermann, Mental states as macrostates emerging from brain electrical dynamics, Chaos: An Interdisciplinary Journal of Nonlinear Science, vol.19, issue.1, p.15102, 2009.
DOI : 10.1063/1.3072788

C. G. Tokman, B. R. Hunt, and P. Wright, Approximating invariant densities of metastablesystems, Ergodic Theory and Dynamical Systems, pp.1345-1361, 2011.

G. Froyland, Statistically optimal almost-invariant sets, Physica D: Nonlinear Phenomena, vol.200, issue.3-4, pp.205-219, 2005.
DOI : 10.1016/j.physd.2004.11.008

P. Beim-graben and A. Hutt, Detecting Recurrence Domains of Dynamical Systems by Symbolic Dynamics, Physical Review Letters, vol.110, issue.15, p.154101, 2013.
DOI : 10.1103/PhysRevLett.110.154101

M. I. Rabinovich, R. Huerta, P. Varona, and V. S. Afraimovich, Transient Cognitive Dynamics, Metastability, and Decision Making, PLoS Computational Biology, vol.7, issue.5, p.1000072, 2008.
DOI : 10.1371/journal.pcbi.1000072.g005

URL : http://doi.org/10.1371/journal.pcbi.1000072

E. N. Lorenz, Deterministic Nonperiodic Flow, Journal of the Atmospheric Sciences, vol.20, issue.2, pp.130-141, 1963.
DOI : 10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2

D. Lehmann, H. Ozaki, and I. Pal, EEG alpha map series: brain micro-states by space-oriented adaptive segmentation, Electroencephalography and Clinical Neurophysiology, vol.67, issue.3, pp.271-288, 1987.
DOI : 10.1016/0013-4694(87)90025-3

J. Wackermann, D. Lehmann, C. M. Michel, and W. K. Strik, Adaptive segmentation of spontaneous EEG map series into spatially defined microstates, International Journal of Psychophysiology, vol.14, issue.3, pp.269-283, 1993.
DOI : 10.1016/0167-8760(93)90041-M

P. Beim-graben and A. Hutt, Detecting event-related recurrences by symbolic analysis: applications to human language processing, Proceedings of the Royal Society London, p.20140089, 2015.
DOI : 10.1016/j.clinph.2005.06.011

URL : https://hal.archives-ouvertes.fr/hal-01077055

B. Gaveau and L. S. Schulman, Multiple phases in stochastic dynamics: Geometry and probabilities, Physical Review E, vol.73, issue.3, 2006.
DOI : 10.1103/PhysRevE.73.036124

R. Shalbaf, H. Behnam, J. Sleigh, D. Steyn-ross, and M. Steyn-ross, Frontal-Temporal Synchronization of EEG Signals Quantified by Order Patterns Cross Recurrence Analysis During Propofol Anesthesia, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.23, issue.3, pp.468-74, 2015.
DOI : 10.1109/TNSRE.2014.2350537

D. Mccarthy, N. Punjabi, P. Kim, C. Frilot, and A. Marino, Recurrence analysis of the EEG during sleep accurately identifies subjects with mental health symptoms, Psychiatry Research: Neuroimaging, vol.224, issue.3, pp.335-375, 2014.
DOI : 10.1016/j.pscychresns.2014.10.004

L. Huang, W. Wang, and S. Singare, Recurrence Quantification Analysis of EEG Predicts Responses to Incision During Anesthesia, Neural Information Processing, ser, pp.58-65, 2006.
DOI : 10.1007/11893295_7

C. Webber and J. Zbilut, Dynamical assessment of physiological systems and states using recurrence plot strategies, J. Appl. Physiol, vol.76, pp.965-973, 1994.

J. Iwanski and E. Bradley, Recurrence plots of experimental data: To embed or not to embed?, Chaos: An Interdisciplinary Journal of Nonlinear Science, vol.8, issue.4, pp.861-871, 1998.
DOI : 10.1063/1.166372

F. Takens, Detecting strange attractors in turbulence, Lecture Notes in Mathematics, vol.20, issue.1, pp.366-381, 1981.
DOI : 10.1007/BF01646553

A. Schnitzler and J. Gross, Normal and pathological oscillatory communication in the brain, Nature Reviews Neuroscience, vol.19, issue.4, p.285296, 2005.
DOI : 10.1016/0166-2236(90)90110-V

H. Poincaré, Sur la probleme des trois corps et leséquationsles´leséquations de la dynamique, Acta Mathematica, vol.13, pp.1-271, 1890.

S. Meignen, T. Oberlin, and S. Mclaughlin, A New Algorithm for Multicomponent Signals Analysis Based on SynchroSqueezing: With an Application to Signal Sampling and Denoising, IEEE Transactions on Signal Processing, vol.60, issue.11, pp.5787-5798, 2012.
DOI : 10.1109/TSP.2012.2212891

F. Auger, P. Flandrin, Y. Lin, S. Mclauhlin, S. Meignen et al., Time-frequency reassignment and synchrosqueezing, IEEE Signal Processing Magazine, vol.80, pp.32-41, 2013.
DOI : 10.1109/msp.2013.2265316

URL : https://hal.archives-ouvertes.fr/hal-00983755

G. Thakur, E. Brevdo, N. Fu?kar, and H. Wu, The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications, Signal Processing, vol.93, issue.5, pp.1079-1094, 2013.
DOI : 10.1016/j.sigpro.2012.11.029

G. Thakur, Synchrosqueezing transform codes 2013, https://github.com/ebrevdo/synchrosqueezing Available: https://github

F. Yates, Contingency Tables Involving Small Numbers and the ?? 2 Test, Supplement to the Journal of the Royal Statistical Society, vol.1, issue.2, pp.217-235, 1934.
DOI : 10.2307/2983604

T. Schreiber and A. Schmitz, Surrogate time series, Physica D: Nonlinear Phenomena, vol.142, issue.3-4, p.346382, 2000.
DOI : 10.1016/S0167-2789(00)00043-9

C. Richard, A. Ferrari, H. Amoud, P. Honeine, P. Flandrin et al., Statistical hypothesis testing with time-frequency surrogates to check signal stationarity, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.3666-3669, 2010.
DOI : 10.1109/ICASSP.2010.5495887

URL : https://hal.archives-ouvertes.fr/ensl-00476017

P. Borgnat, P. Flandrin, P. Honeine, C. Richard, and J. Xiao, Testing Stationarity With Surrogates: A Time-Frequency Approach, IEEE Transactions on Signal Processing, vol.58, issue.7, pp.3459-3470, 2010.
DOI : 10.1109/TSP.2010.2043971

URL : https://hal.archives-ouvertes.fr/ensl-00475929

M. Rabinovich, R. Huerta, P. Varona, and V. Afraimovich, Transient Cognitive Dynamics, Metastability, and Decision Making, PLoS Computational Biology, vol.7, issue.5, 2008.
DOI : 10.1371/journal.pcbi.1000072.g005

C. Skarda and W. Freeman, How brains make chaos in order to make sense of the world, Behavioral and Brain Sciences, vol.9, issue.3, p.161, 1987.
DOI : 10.1016/0006-8993(80)90149-3

E. Basar, Brain Function and Oscillations, Chaos in Brain Function, pp.169-198, 2006.

K. Sellers, D. V. Bennett, A. Hutt, and F. Frohlich, Anesthesia differentially modulates spontaneous network dynamics by cortical area and layer, Journal of Neurophysiology, vol.110, issue.12, pp.2739-2751, 2013.
DOI : 10.1152/jn.00404.2013

URL : https://hal.archives-ouvertes.fr/hal-00872155

K. Sellers, D. V. Bennett, and F. Frohlich, Frequency-band signatures of visual responses to naturalistic input in ferret primary visual cortex during free viewing, Brain Research, vol.1598, pp.31-45, 2015.
DOI : 10.1016/j.brainres.2014.12.016

K. Sellers, D. Bennett, A. Hutt, J. Williams, and F. Frohlich, Awake versus anesthetized: Layer-specific sensory processing in visual cortex and functional connectivity between cortical areas, J. Neurophysiol, p.p. in press, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01140184

A. Arieli, D. Shoham, R. Hildesheim, and A. Grinvald, Coherent spatio-temporal pattern of on-going activity revealed by real-time optical imaging coupled with single unit recording in the cat visual cortex, J. Neurophysiol, vol.73, pp.2072-2093, 1995.

M. Hashemi, A. Hutt, and J. Sleigh, Anesthetic action on extra-synaptic receptors: effects in neural population models of EEG activity, Frontiers in Systems Neuroscience, vol.63, issue.46, 2014.
DOI : 10.1124/mol.63.1.2

URL : https://hal.archives-ouvertes.fr/hal-01088062

M. Alkire, R. J. Haier, and J. H. Fallon, Toward a Unified Theory of Narcosis: Brain Imaging Evidence for a Thalamocortical Switch as the Neurophysiologic Basis of Anesthetic-Induced Unconsciousness, Consciousness and Cognition, vol.9, issue.3, pp.370-386, 2000.
DOI : 10.1006/ccog.1999.0423

S. Ching, A. Cimenser, P. L. Purdon, E. N. Brown, and N. J. , Thalamocortical model for a propofol-induced ??-rhythm associated with loss of consciousness, Proc. Natl. Acad. Sci. USA, pp.22-665, 2010.
DOI : 10.1073/pnas.1017069108

M. Lundqvist, P. Herman, and A. Lansner, Effect of Prestimulus Alpha Power, Phase, and Synchronization on Stimulus Detection Rates in a Biophysical Attractor Network Model, Journal of Neuroscience, vol.33, issue.29, pp.11-917, 2013.
DOI : 10.1523/JNEUROSCI.5155-12.2013

R. Van-rullen, N. Busch, J. Drewes, and J. Dubois, Ongoing eeg phase as a trial-by-trial predictor of perceptual and attentional variability, Front. Psychol, vol.2, p.60, 2011.

V. Romei, V. Brodbeck, C. Michel, A. Amedi, A. Pascual-leone et al., Spontaneous Fluctuations in Posterior ??-Band EEG Activity Reflect Variability in Excitability of Human Visual Areas, Cerebral Cortex, vol.18, issue.9, pp.2010-2018, 2008.
DOI : 10.1093/cercor/bhm229

C. Li, G. Ding, G. Wu, and C. Poon, Band-phase-randomized surrogate data reveal high-frequency chaos in heart rate variability, Conf. Proc. IEEE Eng. Med. Biol. Soc, vol.2010, pp.2806-2809, 2010.