H. D. Abarbanel, R. Brown, J. J. Sidorowich, and L. S. Tsimring, The analysis of observed chaotic data in physical systems, Reviews of Modern Physics, vol.65, issue.4, pp.1331-1392, 1993.
DOI : 10.1103/RevModPhys.65.1331

M. Aboy, R. Hornero, D. Abasolo, and D. Alvarez, Interpretation of the Lempel-Ziv Complexity Measure in the Context of Biomedical Signal Analysis, IEEE Transactions on Biomedical Engineering, vol.53, issue.11, pp.2282-2288, 2006.
DOI : 10.1109/TBME.2006.883696

R. Acharya, U. , O. Faust, N. Kannathal, T. Chua et al., Non-linear analysis of EEG signals at various sleep stages, Computer Methods and Programs in Biomedicine, vol.80, issue.1, pp.37-45, 2005.
DOI : 10.1016/j.cmpb.2005.06.011

P. S. Addison, The Illustrated Wavelet Transform Handbook: Introductory Theory and Applications in Science, Engineering, Medicine and Finance, Bristol: Inst. of Physics Publ, p.353, 2002.
DOI : 10.1201/9781420033397

P. M. Addo, M. Billio, and D. Guégan, Nonlinear dynamics and recurrence plots for detecting financial crisis, The North American Journal of Economics and Finance, vol.26, pp.416-435, 2013.
DOI : 10.1016/j.najef.2013.02.014

URL : https://hal.archives-ouvertes.fr/hal-00964975

O. Akeju, K. J. Pavone, M. B. Westover, R. Vazquez, M. J. Prerau et al., A Comparison of Propofol- and Dexmedetomidine-induced Electroencephalogram Dynamics Using Spectral and Coherence Analysis, Anesthesiology, vol.121, issue.5, pp.978-989, 2014.
DOI : 10.1097/ALN.0000000000000419

W. P. Akrawi, J. C. Drummond, C. J. Kalkman, and P. M. Patel, A Comparison of the Electrophysiologic Characteristics of EEG Burst-Suppression as Produced by Isoflurane, Thiopental, Etomidate, and Propofol, Journal of Neurosurgical Anesthesiology, vol.8, issue.1, pp.40-46, 1996.
DOI : 10.1097/00008506-199601000-00010

M. T. Alkire, A. G. Hudetz, and G. Tononi, Consciousness and Anesthesia, Consciousness and anesthesia, pp.876-880, 2008.
DOI : 10.1126/science.1149213

K. K. Ang, Z. Y. Chin, C. Wang, C. Guan, and H. Zhang, Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b, Frontiers in Neuroscience, vol.6, p.39, 2012.
DOI : 10.3389/fnins.2012.00039

URL : http://doi.org/10.3389/fnins.2012.00039

A. Arcentales, B. F. Giraldo, P. Caminal, S. Benito, and A. Voss, Recurrence quantification analysis of heart rate variability and respiratory flow series in patients on weaning trials, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.2724-2727, 2011.
DOI : 10.1109/IEMBS.2011.6090747

P. Arhem, G. Klement, and J. Nilsson, Mechanisms of Anesthesia: Towards Integrating Network, Cellular, and Molecular Level Modeling, Neuropsychopharmacology, vol.28, issue.S1, pp.40-47, 2003.
DOI : 10.1038/sj.npp.1300142

F. Auger and P. Flandrin, Improving the readability of time-frequency and time-scale representations by the reassignment method, Signal Processing, pp.1068-1089, 1995.
DOI : 10.1109/78.382394

«. Wu, Time-frequency reassignment and synchrosqueezing: An overview, IEEE Signal Processing Magazine, vol.30, issue.6, pp.32-41, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00983755

P. Bak, C. Tang, and K. Wiesenfeld, noise, Physical Review Letters, vol.59, issue.4, pp.381-384, 1987.
DOI : 10.1103/PhysRevLett.59.381

R. Baker, T. C. Gent, Q. Yang, S. Parker, A. L. Vyssotski et al., Altered Activity in the Central Medial Thalamus Precedes Changes in the Neocortex during Transitions into Both Sleep and Propofol Anesthesia, Journal of Neuroscience, vol.34, issue.40, pp.13-326, 2014.
DOI : 10.1523/JNEUROSCI.1519-14.2014

E. Ba?ar, Brain Function and Oscillations. Volume I: Brain Oscillations. Principles and Approaches, red. by H. Haken, ser, p.363, 1998.

R. Battiti, Using mutual information for selecting features in supervised neural net learning, IEEE Transactions on Neural Networks, vol.5, issue.4, pp.537-550, 1994.
DOI : 10.1109/72.298224

C. Bédard and A. Destexhe, Macroscopic Models of Local Field Potentials and the Apparent 1/f Noise in Brain Activity, Biophysical Journal, vol.96, issue.7, pp.2589-2603, 2009.
DOI : 10.1016/j.bpj.2008.12.3951

C. Bedard, H. Kroeger, and A. Destexhe, « Does the 1/f frequency scaling of brain signals reflect self-organized critical states? », Physical review letters, pp.118-102, 2006.

P. Beim-graben, Recurrence complexity analyses of anesthetic EEG data », Talk presented at Neurosys Team, 2015.

P. Beim-graben and A. Hutt, Detecting Recurrence Domains of Dynamical Systems by Symbolic Dynamics, Physical Review Letters, vol.110, issue.15, pp.154-101, 2013.
DOI : 10.1103/PhysRevLett.110.154101

P. Beim-graben and A. Hutt, Detecting event-related recurrences by symbolic analysis: applications to human language processing, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.116, issue.10, pp.20-089, 2015.
DOI : 10.1016/j.clinph.2005.06.011

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

P. Beim-graben, K. K. Sellers, F. Fröhlich, and A. Hutt, Optimal estimation of recurrence structures from time series, EPL (Europhysics Letters), vol.114, issue.3, pp.38-41, 2016.
DOI : 10.1209/0295-5075/114/38003

C. Bennett, L. J. Voss, J. P. Barnard, and J. W. , Sleigh, « Practical use of the raw electroencephalogram waveform during general anesthesia: The art and science, Anesthesia & Analgesia, pp.539-550, 2009.

C. M. Bishop, Pattern Recognition and Machine Learning, ser. Information science and statistics, p.738, 2006.

B. V. Bonnlander and A. S. Weigend, Selecting input variables using mutual information and nonparametric density estimation, Proceedings of the 1994 International Symposium on Artificial Neural Networks, pp.42-50, 1994.

B. E. Boser, I. M. Guyon, and V. N. , Vapnik, « A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory, pp.144-152, 1992.
DOI : 10.1145/130385.130401

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.103.1189

T. J. Brennan, P. K. Zahn, and E. M. Pogatzki-zahn, Mechanisms of Incisional Pain, Mechanisms of incisional pain, pp.1-20, 2005.
DOI : 10.1016/j.atc.2004.11.009

C. J. Burges, « A tutorial on support vector machines for pattern recognition », Data mining and knowledge discovery, pp.121-167, 1998.

G. Buzsaki, Neuronal Oscillations in Cortical Networks, Neuronal oscillations in cortical networks, pp.1926-1929, 2004.
DOI : 10.1126/science.1099745

A. M. Calvão and T. J. Penna, The double pendulum: a numerical study, European Journal of Physics, vol.36, issue.4, pp.45-63, 2015.
DOI : 10.1088/0143-0807/36/4/045018

J. Capon, High-resolution frequency-wavenumber spectrum analysis, Proceedings of the IEEE, pp.1408-1418, 1969.
DOI : 10.1109/PROC.1969.7278

T. W. Chow and D. Huang, Estimating Optimal Feature Subsets Using Efficient Estimation of High-Dimensional Mutual Information, IEEE Transactions on Neural Networks, vol.16, issue.1, pp.213-224, 2005.
DOI : 10.1109/TNN.2004.841414

L. Cohen, Time-frequency distributions-a review, Proceedings of the IEEE, pp.941-981, 1989.
DOI : 10.1109/5.30749

P. A. Devijver and J. Kittler, Pattern Recognition: A Statistical Approach, p.472, 1982.

R. Donner, U. Hinrichs, and B. Scholz-reiter, Symbolic recurrence plots: A new quantitative framework for performance analysis of manufacturing networks, The European Physical Journal Special Topics, vol.164, issue.1, pp.85-104, 2008.
DOI : 10.1140/epjst/e2008-00836-2

G. Dumermuth and L. Molinari, Spectral Analysis of the EEG, Neuropsychobiology, vol.17, issue.1-2, pp.85-99, 1987.
DOI : 10.1159/000118345

J. Eckmann, S. O. Kamphorst, and D. Ruelle, Recurrence Plots of Dynamical Systems, Europhysics Letters (EPL), vol.4, issue.9, pp.973-977, 1987.
DOI : 10.1209/0295-5075/4/9/004

S. Boustani and A. Destexhe, BRAIN DYNAMICS AT MULTIPLE SCALES: CAN ONE RECONCILE THE APPARENT LOW-DIMENSIONAL CHAOS OF MACROSCOPIC VARIABLES WITH THE SEEMINGLY STOCHASTIC BEHAVIOR OF SINGLE NEURONS?, International Journal of Bifurcation and Chaos, vol.20, issue.06, pp.1687-1702, 2010.
DOI : 10.1142/S0218127410026769

URL : https://hal.archives-ouvertes.fr/hal-01179754

M. Farge, Wavelet Transforms and their Applications to Turbulence, Annual Review of Fluid Mechanics, vol.24, issue.1, pp.395-458, 1992.
DOI : 10.1146/annurev.fl.24.010192.002143

URL : https://hal.archives-ouvertes.fr/hal-01299264

P. Faure and A. Lesne, RECURRENCE PLOTS FOR SYMBOLIC SEQUENCES, International Journal of Bifurcation and Chaos, vol.20, issue.06, pp.1731-1749, 2010.
DOI : 10.1142/S0218127410026794

R. Ferenets, T. Lipping, P. Suominen, J. Turunen, P. Puumala et al., Comparison of the Properties of EEG Spindles in Sleep and Propofol Anesthesia, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, pp.6356-6359, 2006.
DOI : 10.1109/IEMBS.2006.259909

R. A. Fisher, THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS, Annals of Eugenics, vol.59, issue.2, pp.179-188, 1936.
DOI : 10.1111/j.1469-1809.1936.tb02137.x

P. Flandrin, F. Auger, and É. , Chassande-Mottin, « Time-frequency reassignment from principles to algorithms, Applications in Time-Frequency Signal Processing, pp.179-203, 2002.

R. Fletcher, Practical Methods of Optimization, p.436, 2008.
DOI : 10.1002/9781118723203

A. M. Fraser and H. L. Swinney, Independent coordinates for strange attractors from mutual information, Physical Review A, vol.33, issue.2, pp.1134-1140, 1986.
DOI : 10.1103/PhysRevA.33.1134

W. J. Freeman and T. Y. Cao, « Proposed Renormalization Group Analysis of Nonlinear Brain Dynamics at Criticality », in Advances in Cognitive Neurodynamics ICCN, pp.145-156, 2007.

R. Friedrich and C. Uhl, Spatio-temporal analysis of human electroencephalograms: Petit-mal epilepsy, Physica D: Nonlinear Phenomena, vol.98, issue.1, pp.171-182, 1996.
DOI : 10.1016/0167-2789(96)00059-0

K. Fukunaga, Introduction to Statistical Pattern Recognition, p.592, 1990.

S. A. Fulop and K. Fitz, Algorithms for computing the time-corrected instantaneous frequency (reassigned) spectrogram, with applications, The Journal of the Acoustical Society of America, vol.119, issue.1, p.360, 2006.
DOI : 10.1121/1.2133000

F. Gabbiani and S. Cox, Mathematics for Neuroscientists, p.486, 2010.

R. Gallager, Information Theory and Reliable Communication, 1972.
DOI : 10.1007/978-3-7091-2945-6

J. D. Gibbons and S. Chakraborti, Nonparametric Statistical Inference, ser. Statistics , textbooks & monographs, Boca Raton, p.630, 2011.

M. Girolami, A. Cichocki, and S. I. Amari, A common neural-network model for unsupervised exploratory data analysis and independent component analysis, IEEE Transactions on Neural Networks, vol.9, issue.6, pp.1495-1501, 1998.
DOI : 10.1109/72.728398

D. Griffiths and J. G. Jones, AWARENESS AND MEMORY IN ANAESTHETIZED PATIENTS, BJA: British Journal of Anaesthesia, vol.65, issue.5, pp.603-606, 1990.
DOI : 10.1093/bja/65.5.603

URL : http://bja.oxfordjournals.org/cgi/content/short/65/5/603

A. Groth, Visualization of coupling in time series by order recurrence plots, Physical Review E, vol.72, issue.4, p.46, 2005.
DOI : 10.1103/PhysRevE.72.046220

I. Guyon and A. Elisseeff, « An introduction to variable and feature selection, The Journal of Machine Learning Research, vol.3, pp.1157-1182, 2003.

I. Guyon, M. Nikravesh, S. Gunn, and L. A. Zadeh, Feature Extraction: Foundations and Applications, red, Studies in Fuzziness and Soft Computing, p.778, 2006.
DOI : 10.1007/978-3-540-35488-8

]. S. Hagihira, Changes in the electroencephalogram during anaesthesia and their physiological basis, British Journal of Anaesthesia, vol.115, issue.suppl 1, pp.27-31
DOI : 10.1093/bja/aev212

S. Hagihira, M. Takashina, T. Mori, H. Ueyama, and T. Mashimo, Electroencephalographic Bicoherence Is Sensitive to Noxious Stimuli during Isoflurane or Sevoflurane Anesthesia, Anesthesiology, vol.100, issue.4, pp.818-825, 2004.
DOI : 10.1097/00000542-200404000-00011

J. A. Hanley and B. J. Mcneil, « The meaning and use of the area under a receiver operating characteristic (ROC) curve. », Radiology, pp.29-36, 1982.

F. J. Harris, On the use of windows for harmonic analysis with the discrete Fourier transform, Proceedings of the IEEE, pp.51-83, 1978.
DOI : 10.1109/PROC.1978.10837

T. Hastie, R. Tibshirani, and J. H. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, p.745, 2009.

F. Hlawatsch and G. F. Boudreaux-bartels, Linear and quadratic time-frequency signal representations, IEEE Signal Processing Magazine, vol.9, issue.2, pp.21-67, 1992.
DOI : 10.1109/79.127284

W. E. Hoffman and G. Edelman, Comparison of isoflurane and desflurane anesthetic depth using burst suppression of the electroencephalogram in neurosurgical patients, Anesthesia & Analgesia, pp.811-816, 1995.

M. Hollander, D. A. Wolfe, and E. Chicken, The Two-Sample Location Problem, Nonparametric Statistical Methods, pp.115-150, 2015.
DOI : 10.1002/9781119196037.ch4

R. Hooke, T. A. Jeeves, and «. , `` Direct Search'' Solution of Numerical and Statistical Problems, Journal of the ACM, vol.8, issue.2, pp.212-229, 1961.
DOI : 10.1145/321062.321069

J. Hu, J. Gao, and J. C. Principe, Analysis of biomedical signals by the Lempel-Ziv complexity: The effect of finite data size, IEEE Transactions on Biomedical Engineering, vol.53, issue.12, pp.2606-2609, 2006.

A. Hutt and T. D. Frank, -Activity of Neural Fields Involving Transmission Delays, Acta Physica Polonica A, vol.108, issue.6, pp.1021-1040, 2005.
DOI : 10.12693/APhysPolA.108.1021

URL : https://hal.archives-ouvertes.fr/hal-01159675

A. J. Izenman, Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning, ser. Springer Texts in Statistics, p.733, 2008.
DOI : 10.1007/978-0-387-78189-1

L. C. Jameson and T. B. Sloan, Using EEG to monitor anesthesia drug effects during surgery, Journal of Clinical Monitoring and Computing, vol.286, issue.6, pp.445-472, 2006.
DOI : 10.1007/s10877-006-9044-x

M. Jedynak, A. J. Pons, and J. Garcia-ojalvo, Cross-frequency transfer in a stochastically driven mesoscopic neuronal model, Frontiers in Computational Neuroscience, vol.12, issue.191, 2015.
DOI : 10.1016/S0006-3495(72)86068-5

J. Jeong and W. J. Williams, Kernel design for reduced interference distributions, IEEE Transactions on Signal Processing, vol.40, issue.2, pp.402-412, 1992.
DOI : 10.1109/78.124950

D. Jordan, R. W. Miksad, and E. J. Powers, Implementation of the continuous wavelet transform for digital time series analysis, Review of Scientific Instruments, vol.68, issue.3, p.1484, 1997.
DOI : 10.1063/1.1147636

H. Kantz and T. Schreiber, Nonlinear Time Series Analysis, p.369, 2004.
DOI : 10.1017/CBO9780511755798

M. B. Kennel and H. D. , Abarbanel, « False neighbors and false strands: A reliable minimum embedding dimension algorithm, Physical Review E, vol.66, issue.2, pp.26-209, 2002.
DOI : 10.1103/physreve.66.026209

M. B. Kennel, R. Brown, and H. D. , Determining embedding dimension for phase-space reconstruction using a geometrical construction, Physical Review A, vol.45, issue.6, pp.3403-3411, 1992.
DOI : 10.1103/PhysRevA.45.3403

S. Kiyama and J. Takeda, Effect of extradural analgesia on the paradoxical arousal response of the electroencephalogram, British Journal of Anaesthesia, vol.79, issue.6, pp.750-753, 1997.
DOI : 10.1093/bja/79.6.750

E. Kochs, P. Bischoff, U. Pichlmeier, J. Schulte, and . Esch, Surgical Stimulation Induces Changes in Brain Electrical Activity during Isoflurane/Nitrous Oxide Anesthesia, Anesthesiology, vol.80, issue.5, pp.1026-1034, 1994.
DOI : 10.1097/00000542-199405000-00012

K. Kodera, R. Gendrin, and C. De-villedary, Analysis of time-varying signals with small BT values », Acoustics, Speech and Signal Processing, IEEE Transactions on, vol.26, issue.1, pp.64-76, 1978.

R. Kohavi and G. H. John, Wrappers for feature subset selection, Artificial Intelligence, vol.97, issue.1-2, pp.273-324, 1997.
DOI : 10.1016/S0004-3702(97)00043-X

URL : http://doi.org/10.1016/s0004-3702(97)00043-x

B. Krese and E. Govekar, Recurrence quantification analysis of intermittent spontaneous to forced dripping transition in laser droplet generation, Chaos, Solitons & Fractals, vol.44, issue.4-5, pp.4-5, 2011.
DOI : 10.1016/j.chaos.2011.02.006

D. Kugiumtzis and N. D. Christophersen, State space reconstruction: method of delays vs singular spectrum approach, 1997.

A. Kumar, A. Bhattacharya, and N. Makhija, Evoked potential monitoring in anaesthesia and analgesia, Anaesthesia, vol.55, issue.3, pp.225-241, 2000.
DOI : 10.1046/j.1365-2044.2000.01120.x

N. Kwak and C. Choi, « Input feature selection by mutual information based on Parzen window », Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.24, issue.12, pp.1667-1671, 2002.

P. Lánský, L. Sacerdote, and F. Tomassetti, On the comparison of Feller and Ornstein-Uhlenbeck models for neural activity, Biological Cybernetics, vol.105, issue.5, pp.457-465, 1995.
DOI : 10.1007/BF00201480

K. Leslie, J. Sleigh, M. J. Paech, L. Voss, C. W. Lim et al., Dreaming and Electroencephalographic Changes during Anesthesia Maintained with Propofol or Desflurane, Anesthesiology, vol.111, issue.3, pp.547-555, 2009.
DOI : 10.1097/ALN.0b013e3181adf768

K. Levenberg, A method for the solution of certain non-linear problems in least squares, Quarterly of Applied Mathematics, vol.2, issue.2, pp.164-168, 1944.
DOI : 10.1090/qam/10666

X. Li, G. Ouyang, X. Yao, and X. Guan, Dynamical characteristics of pre-epileptic seizures in rats with recurrence quantification analysis, Physics Letters A, vol.333, issue.1-2, pp.164-171, 2004.
DOI : 10.1016/j.physleta.2004.10.028

W. Liebert and H. G. Schuster, Proper choice of the time delay for the analysis of chaotic time series, Physics Letters A, vol.142, issue.2-3, pp.107-111, 1989.
DOI : 10.1016/0375-9601(89)90169-2

E. N. Lorenz, Deterministic Nonperiodic Flow, Deterministic nonperiodic flow, pp.130-141, 1963.
DOI : 10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2

S. G. Mallat, A Wavelet Tour of Signal Processing: The Sparse Way, p.805, 2009.

D. W. Marquardt, An Algorithm for Least-Squares Estimation of Nonlinear Parameters, Journal of the Society for Industrial and Applied Mathematics, vol.11, issue.2, pp.431-441, 1963.
DOI : 10.1137/0111030

R. Martin, Noise power spectral density estimation based on optimal smoothing and minimum statistics, IEEE Transactions on Speech and Audio Processing, vol.9, issue.5, pp.504-512, 2001.
DOI : 10.1109/89.928915

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.468.6639

V. Martinez and D. Fletcher, II. Prevention of opioid-induced hyperalgesia in surgical patients: does it really matter?, British Journal of Anaesthesia, vol.109, issue.3, pp.302-304, 2012.
DOI : 10.1093/bja/aes278

URL : https://hal.archives-ouvertes.fr/hal-00614895

N. Marwan, M. Carmenromano, M. Thiel, and J. Kurths, Recurrence plots for the analysis of complex systems, Physics Reports, vol.438, issue.5-6, pp.5-6, 2007.
DOI : 10.1016/j.physrep.2006.11.001

N. Marwan, N. Wessel, U. Meyerfeldt, A. Schirdewan, and J. Kurths, « Recurrence-plotbased measures of complexity and their application to heart-rate-variability data, Physical Review E, vol.66, issue.2, pp.26-702, 2002.

M. D. Mckenzie, Chaotic behavior in national stock market indices, Chaotic behavior in national stock market indices, pp.35-53, 2001.
DOI : 10.1016/S1044-0283(01)00024-2

S. D. Meyers, B. G. Kelly, and J. J. O-'brien, An Introduction to Wavelet Analysis in Oceanography and Meteorology: With Application to the Dispersion of Yanai Waves, Monthly Weather Review, vol.121, issue.10, pp.2858-2866, 1993.
DOI : 10.1175/1520-0493(1993)121<2858:AITWAI>2.0.CO;2

E. Niedermeyer, The normal EEG of the waking adult », Electroencephalography: Basic principles, clinical applications, and related fields, 2005.

A. V. Oppenheim and R. W. Schafer, Discrete-time signal processing, ser. Prentice Hall signal processing series, p.pp, 1989.

K. A. Otto and P. Mally, Noxious stimulation during orthopaedic surgery results in EEG ???arousal??? or ???paradoxical arousal??? reaction in isoflurane-anaesthetised sheep, Research in Veterinary Science, vol.75, issue.2, pp.103-112, 2003.
DOI : 10.1016/S0034-5288(03)00077-8

N. H. Packard, J. P. Crutchfield, J. D. Farmer, and R. S. Shaw, Geometry from a Time Series, Geometry from a time series, pp.712-716, 1980.
DOI : 10.1103/PhysRevLett.45.712

H. Peng, F. Long, and C. Ding, Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.8, pp.1226-1238, 2005.
DOI : 10.1109/TPAMI.2005.159

D. M. Philbin, C. E. Rosow, R. C. Schneider, G. Koski, and M. N. , Fentanyl and Sufentanil Anesthesia Revisited, Anesthesiology, vol.73, issue.1, pp.5-11, 1990.
DOI : 10.1097/00000542-199007000-00002

M. Plancherel and M. Leffler, « Contribution à l'étude de la représentation d'une fonction arbitraire par des intégrales définies, Rendiconti del Circolo Matematico di Palermo, pp.289-335, 1884.

H. Poincaré, « Sur le problème des trois corps et les équations de la dynamique, Acta mathematica, pp.3-270, 1890.

R. Poli, J. Kennedy, and T. Blackwell, Particle swarm optimization, Swarm Intelligence, pp.33-57, 2007.
DOI : 10.1007/s11721-007-0002-0

URL : https://hal.archives-ouvertes.fr/hal-01169049

P. L. Purdon, E. T. Pierce, E. Mukamel, M. J. Prerau, J. L. Walsh et al., Electroencephalogram signatures of loss and recovery of consciousness from propofol, Proceedings of the National Academy of Sciences of the United States of America, pp.1142-1151, 2013.
DOI : 10.1073/pnas.1221180110

J. R. Quinlan, Induction of decision trees, Machine Learning, pp.81-106, 1986.
DOI : 10.1007/BF00116251

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.167.3624

M. 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

M. I. Rabinovich, R. Huerta, P. Varona, and V. S. Afraimovich, Transient Cognitive Dynamics, Metastability, and Decision Making, Transient cognitive dynamics, metastability, and decision making, p.1000072, 2008.
DOI : 10.1371/journal.pcbi.1000072.g005

I. J. Rampil and R. S. Matteo, Changes in EEG Spectral Edge Frequency Correlate with the Hemodynamic Response to Laryngoscopy and Intubation, Anesthesiology, vol.67, issue.1, pp.139-142, 1987.
DOI : 10.1097/00000542-198707000-00033

M. Rodriguez-fernandez, J. A. Egea, and J. R. Banga, « Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems, BMC Bioinformatics, vol.7, issue.1, p.483, 2006.
DOI : 10.1186/1471-2105-7-483

Y. Saeys, I. Inza, and P. Larranaga, A review of feature selection techniques in bioinformatics, Bioinformatics, vol.23, issue.19, pp.2507-2517, 2007.
DOI : 10.1093/bioinformatics/btm344

M. Schartner, A. Seth, Q. Noirhomme, M. Boly, M. Bruno et al., Complexity of Multi-Dimensional Spontaneous EEG Decreases during Propofol Induced General Anaesthesia, PLOS ONE, vol.1, issue.1, pp.1-21, 2015.
DOI : 10.1371/journal.pone.0133532.s002

S. Schinkel, N. Marwan, and J. Kurths, Order patterns recurrence plots in the analysis of ERP data, Cognitive Neurodynamics, vol.223, issue.4, pp.317-325, 2007.
DOI : 10.1007/s11571-007-9023-z

A. Schuster, On the investigation of hidden periodicities with application to a supposed 26 day period of meteorological phenomena, Journal of Geophysical Research, vol.91, issue.1, p.13, 1898.
DOI : 10.1029/TM003i001p00013

R. S. Schwartz, E. N. Brown, R. Lydic, and N. D. Schiff, General Anesthesia, Sleep, and Coma, General anesthesia, sleep, and coma, pp.2638-2650, 2010.
DOI : 10.1056/NEJMra0808281

P. K. Sinha and T. Koshy, « Monitoring devices for measuring the depth of anaesthesiaan overview, Indian Journal of Anaesthesia, vol.51, issue.5, p.365, 2007.

C. A. Skarda and W. J. 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

J. W. Sleigh, K. Leslie, and L. Voss, The effect of skin incision on the electroencephalogram during general anesthesia maintained with propofol or desflurane, Journal of Clinical Monitoring and Computing, vol.9, issue.4, pp.307-325, 2010.
DOI : 10.1007/s10877-010-9251-3

P. Stoica and R. L. Moses, Spectral analysis of signals, p.452, 2005.

F. Takens, Detecting strange attractors in turbulence, Dynamical Systems and Turbulence, pp.366-381, 1980.
DOI : 10.1007/BF01646553

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

R. Tibshirani, « Regression Shrinkage and Selection via the Lasso », Journal of the Royal Statistical Society. Series B (Methodological), vol.58, issue.1, pp.267-288, 1996.
DOI : 10.1111/j.1467-9868.2011.00771.x

K. Torkkola, « Feature extraction by non parametric mutual information maximization, The Journal of Machine Learning Research, vol.3, pp.1415-1438, 2003.
DOI : 10.1109/icassp.2002.5743865

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.24.7256

C. Torrence and G. P. Compo, A Practical Guide to Wavelet Analysis, Bulletin of the American Meteorological Society, vol.79, issue.1, pp.61-78, 1998.
DOI : 10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2

T. To?i?, K. K. Sellers, F. Fröhlich, M. Fedotenkova, P. Beim-graben et al., Statistical Frequency-Dependent Analysis of Trial-to-Trial Variability in Single Time Series by Recurrence Plots, Frontiers in Systems Neuroscience, p.184, 2016.
DOI : 10.1371/journal.pcbi.1002303

G. E. Uhlenbeck and L. S. Ornstein, On the Theory of the Brownian Motion, Physical Review, vol.36, issue.5, pp.823-841, 1930.
DOI : 10.1103/PhysRev.36.823

J. R. Vergara and P. A. Estévez, A review of feature selection methods based on mutual information, Neural Computing and Applications, pp.175-186, 2014.
DOI : 10.1007/s00521-013-1368-0

C. L. Webber and J. P. Zbilut, « Dynamical assessment of physiological systems and states using recurrence plot strategies, Journal of Applied Physiology, vol.76, issue.2, pp.965-973, 1994.

P. D. 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.
DOI : 10.1109/TAU.1967.1161901

M. A. Whittington, R. D. Traub, and J. G. Jefferys, Synchronized oscillations in interneuron networks driven by metabotropic glutamate receptor activation, Nature, vol.373, issue.6515, pp.612-615, 1995.
DOI : 10.1038/373612a0

J. Xiao and P. Flandrin, Multitaper Time-Frequency Reassignment for Nonstationary Spectrum Estimation and Chirp Enhancement, IEEE Transactions on Signal Processing, vol.55, issue.6, pp.2851-2860, 2007.
DOI : 10.1109/TSP.2007.893961

URL : https://hal.archives-ouvertes.fr/ensl-00111665

J. Yan, Y. Wang, G. Ouyang, T. Yu, and X. Li, Using max entropy ratio of recurrence plot to measure electrocorticogram changes in epilepsy patients, Physica A: Statistical Mechanics and its Applications, pp.109-116, 2016.
DOI : 10.1016/j.physa.2015.09.069

J. P. Zbilut and C. L. Webber, Embeddings and delays as derived from quantification of recurrence plots, Physics Letters A, vol.171, issue.3-4, pp.199-203, 1992.
DOI : 10.1016/0375-9601(92)90426-M

A. M. Zbinden, S. Petersen-felix, and D. A. Thomson, Anesthetic Depth Defined Using Multiple Noxious Stimuli during Isoflurane/Oxygen Anesthesia, Anesthesiology, vol.80, issue.2, pp.261-267, 1994.
DOI : 10.1097/00000542-199402000-00005