E. Carlstein, The use of subseries values for estimating the variance of a general statistic from a stationary sequence, The Annals of Statistics, pp.1171-1179, 1986.

J. Carp, The secret lives of experiments: methods reporting in the fmri literature, Neuroimage, vol.63, issue.1, pp.289-300, 2012.

R. Chalasani, J. C. Principe, R. , and N. , A fast proximal method for convolutional sparse coding, International Joint Conference on Neural Networks (IJCNN), pp.1-5, 2013.

J. M. Bibliography-chambers, C. L. Mallows, and B. W. Stuck, A method for simulating stable random variables, Journal of the american statistical association, vol.71, issue.354, pp.340-344, 1976.

K. S. Chan and H. Tong, On estimating thresholds in autoregressive models, Journal of Time Series Analysis, vol.7, issue.3, pp.179-190, 1986.

E. F. Chang, J. W. Rieger, K. Johnson, M. S. Berger, N. M. Barbaro et al., Categorical speech representation in human superior temporal gyrus, Nature neuroscience, vol.13, issue.11, p.1428, 2010.

M. Chavez, M. Besserve, C. Adam, M. , and J. , Towards a proper estimation of phase synchronization from time series, Journal of Neuroscience methods, vol.154, issue.1, pp.149-160, 2006.

M. Chehelcheraghi, C. Van-leeuwen, E. Steur, C. , and N. , A neural mass model of cross frequency coupling, PLoS ONE, vol.12, issue.4, p.173776, 2017.

C. W. Chen and M. K. So, On a threshold heteroscedastic model, International Journal of Forecasting, vol.22, issue.1, pp.73-89, 2006.

R. Chen and R. S. Tsay, Functional-coefficient autoregressive models, Journal of the American Statistical Association, vol.88, issue.421, pp.298-308, 1993.

T. Chi, P. Ru, and S. A. Shamma, Multiresolution spectrotemporal analysis of complex sounds, The Journal of the Acoustical Society of America, vol.118, issue.2, pp.887-906, 2005.

S. Chib and E. Greenberg, Understanding the Metropolis-Hastings algorithm, The American Statistician, vol.49, issue.4, pp.327-335, 1995.

D. Cohen, Magnetoencephalography: evidence of magnetic fields produced by alpha-rhythm currents, Science, vol.161, issue.3843, pp.784-786, 1968.

M. X. Cohen, Multivariate cross-frequency coupling via generalized eigendecomposition. eLife, vol.6, p.21792, 2017.

S. R. Cole, E. J. Peterson, R. Van-der-meij, C. De-hemptinne, P. A. Starr et al., Nonsinusoidal oscillations underlie pathological phase-amplitude coupling in the motor cortex in parkinson's disease. bioRxiv, p.49304, 2016.

S. R. Cole and B. Voytek, Brain oscillations and the importance of waveform shape, Trends in Cognitive Sciences, 2017.

L. L. Colgin, T. Denninger, M. Fyhn, T. Hafting, T. Bonnevie et al., Frequency of gamma oscillations routes flow of information in the hippocampus, Nature, vol.462, issue.7271, pp.353-357, 2009.

R. Dahlhaus, On the Kullback-Leibler information divergence of locally stationary processes, Stochastic Processes and their Applications, vol.62, pp.139-168, 1996.

G. Dallérac, M. Graupner, J. Knippenberg, R. C. Martinez, T. F. Tavares et al., Updating temporal expectancy of an aversive event engages striatal plasticity under amygdala control, Nature Communications, vol.8, p.13920, 2017.

I. Daubechies, Orthonormal bases of compactly supported wavelets, Communications on pure and applied mathematics, vol.41, pp.909-996, 1988.

H. Davis, P. A. Davis, A. L. Loomis, E. N. Harvey, H. et al., Electrical reactions of the human brain to auditory stimulation during sleep, Journal of Neurophysiology, vol.2, issue.6, pp.500-514, 1939.

D. A. Depireux, J. Z. Simon, D. J. Klein, and S. A. Shamma, Spectro-temporal response field characterization with dynamic ripples in ferret primary auditory cortex, Journal of neurophysiology, vol.85, issue.3, pp.1220-1234, 2001.

C. A. Desoer, Slowly varying discrete system xi+1=Ai xi, Electronics Letters, vol.6, issue.11, pp.339-340, 1970.

D. V. Dijk, T. Teräsvirta, and P. H. Franses, Smooth transition autoregressive models-a survey of recent developments, Econometric reviews, vol.21, issue.1, pp.1-47, 2002.

S. I. Dimitriadis, Y. Sun, N. Thakor, and A. Bezerianos, Mining cross-frequency coupling microstates (CFCµstates) from EEG recordings during resting state and mental arithmetic tasks, IEEE 38th Annual International Conference of the, pp.5517-5520, 2016.

M. N. Do and M. Vetterli, The contourlet transform: an efficient directional multiresolution image representation, IEEE Transactions on image processing, vol.14, issue.12, pp.2091-2106, 2005.

D. L. Donoho and J. M. Johnstone, Ideal spatial adaptation by wavelet shrinkage, biometrika, vol.81, issue.3, pp.425-455, 1994.

T. Dupré-la-tour, Y. Grenier, G. , and A. , Parametric estimation of spectrum driven by an exogenous signal, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.4301-4305, 2017.

T. Dupré-la-tour, Y. Grenier, G. , and A. , Driver estimation in nonlinear autoregressive models, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.

T. Dupré-la-tour, T. Moreau, M. Jas, G. , and A. , Multivariate convolutional sparse coding for electromagnetic brain signals, Advances in Neural Information Processing Systems (NIPS), 2018.

T. Dupré-la-tour, L. Tallot, L. Grabot, V. Doyère, V. Van-wassenhove et al., Non-linear auto-regressive models for cross-frequency coupling in neural time series, PLOS Computational Biology, issue.12, p.13, 2017.

J. Durbin, The fitting of time-series models, Review of the Int. statistical institute, pp.233-244, 1960.

D. Dvorak and A. A. Fenton, Toward a proper estimation of phase-amplitude coupling in neural oscillations, Journal of Neuroscience methods, vol.225, pp.42-56, 2014.

G. T. Einevoll, H. Lindén, T. Tetzlaff, S. ?eski, and K. H. Pettersen, Local field potentials. Principles of Neural Coding, p.37, 2013.

M. Elad and M. Aharon, Image denoising via sparse and redundant representations over learned dictionaries, IEEE Transactions on Image processing, vol.15, issue.12, pp.3736-3745, 2006.

R. F. Engle, Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation, Econometrica: Journal of the Econometric Society, pp.987-1007, 1982.

H. G. Feichtinger and T. Strohmer, Gabor analysis and algorithms: Theory and applications, 2012.

J. Fell and N. Axmacher, The role of phase synchronization in memory processes, Nature reviews. Neuroscience, vol.12, issue.2, p.105, 2011.

C. Févotte, N. Bertin, and J. Durrieu, Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis, Neural computation, vol.21, issue.3, pp.793-830, 2009.

E. Florin and S. Baillet, The brain's resting-state activity is shaped by synchronized cross-frequency coupling of neural oscillations, NeuroImage, vol.111, pp.26-35, 2015.

J. Friedman, T. Hastie, H. Höfling, and R. Tibshirani, Pathwise coordinate optimization, The Annals of Applied Statistics, vol.1, issue.2, pp.302-332, 2007.

P. Fries, A mechanism for cognitive dynamics: neuronal communication through neuronal coherence, Trends in cognitive sciences, vol.9, issue.10, pp.474-480, 2005.

P. Fries, Rhythms for cognition: communication through coherence, Neuron, vol.88, issue.1, pp.220-235, 2015.

P. Fries, J. H. Reynolds, A. E. Rorie, and R. Desimone, Modulation of oscillatory neuronal synchronization by selective visual attention, Science, vol.291, issue.5508, pp.1560-1563, 2001.

K. Fukunaga, Introduction to statistical pattern recognition, 2013.

C. Garcia-cardona and B. Wohlberg, Convolutional dictionary learning, 2017.

E. M. Gerber, B. Sadeh, A. Ward, R. T. Knight, and L. Y. Deouell, Nonsinusoidal activity can produce cross-frequency coupling in cortical signals in the absence of functional interaction between neural sources, PloS one, vol.11, issue.12, p.167351, 2016.

B. Gips, A. Bahramisharif, E. Lowet, M. Roberts, P. De-weerd et al., Discovering recurring patterns in electrophysiological recordings, J. Neurosci. Methods, vol.275, pp.66-79, 2017.

C. Giraud, F. Roueff, and A. Sanchez-perez, Aggregation of predictors for nonstationary sub-linear processes and online adaptive forecasting of time varying autoregressive processes, The Annals of Statistics, vol.43, issue.6, pp.2412-2450, 2015.

S. Godsill and E. Kuruoglu, Bayesian inference for time series with heavy-tailed symmetric ?-stable noise processes, Proc. Applications of heavy tailed distributions in economics, eng. and stat, 1999.

J. Gorski, F. Pfeuffer, and K. Klamroth, Biconvex sets and optimization with biconvex functions: a survey and extensions, Mathematical Methods of Operations Research, vol.66, issue.3, pp.373-407, 2007.

A. Gramfort, M. Luessi, E. Larson, D. A. Engemann, D. Strohmeier et al., MEG and EEG data analysis with MNE-python, Frontiers in neuroscience, vol.7, p.267, 2013.

A. Gramfort, M. Luessi, E. Larson, D. A. Engemann, D. Strohmeier et al., Mne software for processing meg and eeg data, Neuroimage, vol.86, pp.446-460, 2014.

C. W. Granger, Investigating causal relations by econometric models and cross-spectral methods, Econometrica: Journal of the Econometric Society, pp.424-438, 1969.

C. W. Granger, Some recent development in a concept of causality, Journal of econometrics, vol.39, issue.1, pp.199-211, 1988.

Y. Grenier, Time-dependent ARMA modeling of nonstationary signals. Acoustics, Speech and Signal Processing, IEEE Transactions on, vol.31, issue.4, pp.899-911, 1983.

Y. Grenier, Modélisation de signaux non-stationnaires, 1984.

Y. Grenier, Estimating an AR model with exogenous driver, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00875064

Y. Grenier and M. Omnes-chevalier, Autoregressive models with timedependent log area ratios. Acoustics, Speech and Signal Processing, IEEE Transactions on, vol.36, issue.10, pp.1602-1612, 1988.

R. Grosse, R. Raina, H. Kwong, and A. Y. Ng, Shift-invariant sparse coding for audio classification, 23rd Conference on Uncertainty in Artificial Intelligence (UAI), pp.149-158, 2007.

S. Haegens, H. Cousijn, G. Wallis, P. J. Harrison, and A. C. Nobre, Inter-and intra-individual variability in alpha peak frequency, Neuroimage, vol.92, pp.46-55, 2014.

V. Haggan and T. Ozaki, Modelling nonlinear random vibrations using an amplitude-dependent autoregressive time series model, Biometrika, vol.68, issue.1, pp.189-196, 1981.

J. D. Hamilton, A new approach to the economic analysis of nonstationary time series and the business cycle, Econometrica: Journal of the Econometric Society, pp.357-384, 1989.

R. Hari, Action-perception connection and the cortical mu rhythm, Progress in brain research, vol.159, pp.253-260, 2006.

R. Hari and A. Puce, , 2017.

T. Hastie, R. Tibshirani, W. , and M. J. , Statistical Learning with Sparsity, 2015.

S. Haufe, R. Tomioka, G. Nolte, K. Müller, K. et al., Modeling sparse connectivity between underlying brain sources for EEG/MEG, IEEE Trans, vol.57, issue.8, pp.1954-1963, 2010.

F. Heide, W. Heidrich, and G. Wetzstein, Fast and flexible convolutional sparse coding, Computer Vision and Pattern Recognition (CVPR), pp.5135-5143, 2015.

A. C. Heusser, D. Poeppel, Y. Ezzyat, and L. Davachi, Episodic sequence memory is supported by a theta-gamma phase code, Nature neuroscience, 2016.

S. Hitziger, M. Clerc, S. Saillet, C. Benar, P. et al., Adaptive waveform learning: A framework for modeling variability in neurophysiological signals, IEEE Transactions on Signal Processing, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01548428

C. R. Holdgraf, W. De-heer, B. Pasley, J. Rieger, N. Crone et al., Rapid tuning shifts in human auditory cortex enhance speech intelligibility, Nature communications, vol.7, p.13654, 2016.

C. R. Holdgraf, J. W. Rieger, C. Micheli, S. Martin, R. T. Knight et al., Encoding and decoding models in cognitive electrophysiology, Frontiers in Systems Neuroscience, vol.11, p.61, 2017.

D. H. Hubel and T. N. Wiesel, Receptive fields, binocular interaction and functional architecture in the cat's visual cortex, The Journal of physiology, vol.160, issue.1, pp.106-154, 1962.

P. J. Huber, Robust Statistics, 1981.

A. Hyafil, Misidentifications of specific forms of cross-frequency coupling: three warnings, Frontiers in Neuroscience, p.9, 2015.

A. Hyafil, A. Giraud, L. Fontolan, G. , and B. , Neural cross-frequency coupling: Connecting architectures, mechanisms, and functions. Trends in Neurosciences, vol.38, pp.725-740, 2015.

M. Jachan, G. Matz, and F. Hlawatsch, Time-frequency ARMA models and parameter estimators for underspread nonstationary random processes, IEEE Transactions on Signal Processing, vol.55, issue.9, pp.4366-4381, 2007.

M. Jas, T. Dupré-la-tour, U. ?im?ekli, G. , and A. , Learning the morphology of brain signals using alpha-stable convolutional sparse coding, Advances in Neural Information Processing Systems 30 (NIPS), pp.1099-1108, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01590988

H. Jasper and W. Penfield, Electrocorticograms in man: effect of voluntary movement upon the electrical activity of the precentral gyrus, Archiv für Psychiatrie und Nervenkrankheiten, vol.183, issue.1-2, pp.163-174, 1949.

H. H. Jasper, Charting the sea of brain waves, Science, vol.108, pp.343-347, 1948.

O. Jensen and L. L. Colgin, Cross-frequency coupling between neuronal oscillations, Trends in cognitive sciences, vol.11, issue.7, pp.267-269, 2007.

O. Jensen, E. Spaak, P. , and H. , Discriminating valid from spurious indices of phase-amplitude coupling. eneuro, p.334, 2016.

H. Jiang, A. Bahramisharif, M. A. Van-gerven, J. , and O. , Measuring directionality between neuronal oscillations of different frequencies, Neuroimage, vol.118, pp.359-367, 2015.

V. Jirsa and V. Müller, Cross-frequency coupling in real and virtual brain networks, Frontiers in computational neuroscience, vol.7, p.78, 2013.

E. Jones, T. Oliphant, and P. Peterson, SciPy: Open source scientific tools for Python, 2001.

S. R. Jones, When brain rhythms aren't 'rhythmic': implication for their mechanisms and meaning, Curr. Opin. Neurobiol, vol.40, pp.72-80, 2016.

P. Jost, P. Vandergheynst, S. Lesage, G. , and R. , Motif: an efficient algorithm for learning translation invariant dictionaries, Acoustics, Speech and Signal Processing, vol.5, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00544911

R. Kaplan, D. Bush, M. Bonnefond, P. A. Bandettini, G. R. Barnes et al., Medial prefrontal theta phase coupling during spatial memory retrieval, Hippocampus, vol.24, issue.6, pp.656-665, 2014.

K. Kavukcuoglu, P. Sermanet, Y. Boureau, K. Gregor, M. Mathieu et al., Learning convolutional feature hierarchies for visual recognition, Advances in Neural Information Processing Systems (NIPS), pp.1090-1098, 2010.

K. N. Kay, T. Naselaris, R. J. Prenger, and J. L. Gallant, Identifying natural images from human brain activity, Nature, vol.452, issue.7185, pp.352-355, 2008.

S. M. Kay and S. L. Marple, Spectrum analysis-a modern perspective. Proceedings of the IEEE, vol.69, pp.1380-1419, 1981.

S. Kellis, K. Miller, K. Thomson, R. Brown, P. House et al., Decoding spoken words using local field potentials recorded from the cortical surface, Journal of neural engineering, vol.7, issue.5, p.56007, 2010.

S. Khan, A. Gramfort, N. Shetty, K. Ganesan, S. Moran et al., Local and long-range functional connectivity is reduced in concert in autism spectrum disorders, Proc. Natl. Acad. Sci, 2013.

D. Khodagholy, J. N. Gelinas, T. Thesen, W. Doyle, O. Devinsky et al., Neurogrid: recording action potentials from the surface of the brain, Nature neuroscience, vol.18, issue.2, pp.310-315, 2015.

Y. Kikuchi, A. Attaheri, B. Wilson, A. E. Rhone, K. V. Nourski et al., Sequence learning modulates neural responses and oscillatory coupling in human and monkey auditory cortex, PLoS biology, vol.15, issue.4, p.2000219, 2017.

M. A. Kramer, A. B. Tort, and N. J. Kopell, Sharp edge artifacts and spurious coupling in EEG frequency comodulation measures, Journal of Neuroscience methods, vol.170, issue.2, pp.352-357, 2008.

E. E. Kuruoglu, Signal processing in ?-stable noise environments: a least Lp-norm approach, 1999.

J. Lachaux, E. Rodriguez, J. Martinerie, and F. J. Varela, Measuring phase synchrony in brain signals, Human brain mapping, vol.8, issue.4, pp.194-208, 1999.

P. Lakatos, A. S. Shah, K. H. Knuth, I. Ulbert, G. Karmos et al., An oscillatory hierarchy controlling neuronal excitability and stimulus processing in the auditory cortex, Journal of neurophysiology, vol.94, issue.3, pp.1904-1911, 2005.

S. Leglaive, U. ?im?ekli, A. Liutkus, R. Badeau, R. et al., Alpha-stable multichannel audio source separation, ICASSP, pp.576-580, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01416366

B. Recht, M. Fazel, and P. A. Parrilo, Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization, SIAM review, vol.52, issue.3, pp.471-501, 2010.

P. Richtárik and M. Taká?, Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function, Mathematical Programming, vol.144, issue.1-2, pp.1-38, 2014.

F. Roux, M. Wibral, W. Singer, J. Aru, and P. J. Uhlhaas, The phase of thalamic alpha activity modulates cortical gamma-band activity: evidence from resting-state MEG recordings, Journal of Neuroscience, vol.33, issue.45, pp.17827-17835, 2013.

W. J. Rugh, Linear system theory, vol.2, 1996.

T. Rukat, A. Baker, A. Quinn, and M. Woolrich, Resting state brain networks from EEG: Hidden Markov states vs. classical microstates, 2016.

G. Samorodnitsky and M. S. Taqqu, Stable non-Gaussian random processes: stochastic models with infinite variance, vol.1, 1994.

R. Schmidt, Multiple emitter location and signal parameter estimation, IEEE transactions on antennas and propagation, vol.34, issue.3, pp.276-280, 1986.

T. Schreiber, Measuring information transfer, Physical review letters, vol.85, issue.2, p.461, 2000.

G. Schwarz, Estimating the dimension of a model, Ann. Stat, vol.6, issue.2, pp.461-464, 1978.

C. E. Shannon and W. Weaver, The mathematical theory of communication, 1949.

P. R. Shirvalkar, P. R. Rapp, and M. L. Shapiro, Bidirectional changes to hippocampal theta-gamma comodulation predict memory for recent spatial episodes, Proceedings of the National Academy of Sciences, vol.107, issue.15, pp.7054-7059, 2010.

E. P. Simoncelli, W. T. Freeman, E. H. Adelson, and D. J. Heeger, Shiftable multiscale transforms. IEEE transactions on Information Theory, vol.38, pp.587-607, 1992.

U. ?im?ekli, A. Liutkus, and A. T. Cemgil, Alpha-stable matrix factorization, IEEE SPL, vol.22, issue.12, pp.2289-2293, 2015.

M. ?orel and F. ?roubek, Fast convolutional sparse coding using matrix inversion lemma, Digital Signal Processing, 2016.

M. Spiridonakos and S. Fassois, Non-stationary random vibration modelling and analysis via functional series time-dependent ARMA (FS-TARMA) models-a critical survey, Mechanical Systems and Signal Processing, vol.47, issue.1, pp.175-224, 2014.

J. Spyers-ashby, P. Bain, and S. Roberts, A comparison of fast fourier transform (FFT) and autoregressive (AR) spectral estimation techniques for the analysis of tremor data, Journal of neuroscience methods, vol.83, issue.1, pp.35-43, 1998.

J. Starck, E. J. Candès, and D. L. Donoho, The curvelet transform for image denoising, IEEE Transactions on image processing, vol.11, issue.6, pp.670-684, 2002.

C. M. Sweeney-reed, T. Zaehle, J. Voges, F. C. Schmitt, L. Buentjen et al., Corticothalamic phase synchrony and cross-frequency coupling predict human memory formation, vol.3, p.5352, 2014.

J. R. Taylor, N. Williams, R. Cusack, T. Auer, M. A. Shafto et al., The cambridge centre for ageing and neuroscience (cam-can) data repository: structural and functional mri, meg, and cognitive data from a cross-sectional adult lifespan sample, Neuroimage, vol.144, pp.262-269, 2017.

F. E. Theunissen, K. Sen, and A. J. Doupe, Spectral-temporal receptive fields of nonlinear auditory neurons obtained using natural sounds, Journal of Neuroscience, vol.20, issue.6, pp.2315-2331, 2000.

R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B (Methodological), pp.267-288, 1996.

H. Tong, Threshold models in time series analysis-30 years on, Statistics and its Interface, vol.4, issue.2, pp.107-118, 2011.

H. Tong and K. S. Lim, Threshold autoregression, limit cycles and cyclical data, Journal of the Royal Statistical Society. Series B (Methodological), pp.245-292, 1980.

A. B. Tort, R. Komorowski, H. Eichenbaum, and N. Kopell, Measuring phase-amplitude coupling between neuronal oscillations of different frequencies, J. Neurophysiol, vol.104, issue.2, pp.1195-1210, 2010.

A. B. Tort, M. A. Kramer, C. Thorn, D. J. Gibson, Y. Kubota et al., Dynamic cross-frequency couplings of local field potential oscillations in rat striatum and hippocampus during performance of a t-maze task, Proc. Natl. Acad. Sci, vol.105, pp.20517-20522, 2008.

A. B. Tort, H. G. Rotstein, T. Dugladze, T. Gloveli, and N. J. Kopell, On the formation of gamma-coherent cell assemblies by oriens lacunosum-moleculare interneurons in the hippocampus, Proceedings of the National Academy of Sciences, vol.104, issue.33, pp.13490-13495, 2007.

L. N. Trefethen, I. Bau, and D. , Numerical linear algebra, vol.50, 1997.

T. Tuomisto, R. Hari, T. Katila, T. Poutanen, and T. Varpula, Studies of auditory evoked magnetic and electric responses: Modality specificity and modelling, Il Nuovo Cimento D, vol.2, issue.2, pp.471-483, 1983.

P. A. Valdés-sosa, J. M. Sánchez-bornot, A. Lage-castellanos, M. Vega-hernández, J. Bosch-bayard et al., Estimating brain functional connectivity with sparse multivariate autoregression, Philosophical Transactions of the Royal Society of London B: Biological Sciences, vol.360, pp.969-981, 1457.

F. Van-ede, A. J. Quinn, M. W. Woolrich, and A. C. Nobre, Neural oscillations: Sustained rhythms or transient burst-events?, Trends in Neurosciences, 2018.

D. C. Van-essen, S. M. Smith, D. M. Barch, T. E. Behrens, E. Yacoub et al., The wu-minn human connectome project: an overview, Neuroimage, vol.80, pp.62-79, 2013.

B. Van-wijk, A. Jha, W. Penny, and V. Litvak, Parametric estimation of cross-frequency coupling, Journal of neuroscience methods, vol.243, pp.94-102, 2015.

A. P. Vaz, R. B. Yaffe, J. H. Wittig, S. K. Inati, and K. A. Zaghloul, Dual origins of measured phase-amplitude coupling reveal distinct neural mechanisms underlying episodic memory in the human cortex, Neuroimage, vol.148, pp.148-159, 2017.

D. Vidaurre, R. Abeysuriya, R. Becker, A. J. Quinn, F. Alfaro-almagro et al., Discovering dynamic brain networks from big data in rest and task, 2017.

D. Vidaurre, A. J. Quinn, A. P. Baker, D. Dupret, A. Tejero-cantero et al., Spectrally resolved fast transient brain states in electrophysiological data, Neuroimage, vol.126, pp.81-95, 2016.

B. Voytek, R. T. Canolty, A. Shestyuk, N. Crone, J. Parvizi et al., Shifts in gamma phase-amplitude coupling frequency from theta to alpha over posterior cortex during visual tasks, Frontiers in human neuroscience, vol.4, p.191, 2010.

H. Wakita, Direct estimation of the vocal tract shape by inverse filtering of acoustic speech waveforms, IEEE Transactions on Audio and Electroacoustics, vol.21, issue.5, pp.417-427, 1973.

W. Wang, A. D. Degenhart, G. P. Sudre, D. A. Pomerleau, and E. C. Tyler-kabara, Decoding semantic information from human electrocorticographic (ecog) signals, Annual International Conference of the IEEE, pp.6294-6298, 2011.

Y. Wang, Y. Qi, Y. Wang, Z. Lei, X. Zheng et al., Delving into ?-stable distribution in noise suppression for seizure detection from scalp EEG, J. Neural. Eng, vol.13, issue.5, p.56009, 2016.

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.