F. Wendling, P. Chauvel, A. Biraben, and F. Bartolomei, From Intracerebral EEG Signals to Brain Connectivity: Identification of Epileptogenic Networks in Partial Epilepsy, Frontiers in Systems Neuroscience, vol.4, issue.154, 2010.
DOI : 10.3389/fnsys.2010.00154

M. Rubinov and O. Sporns, Complex network measures of brain connectivity: Uses and interpretations, NeuroImage, vol.52, issue.3, pp.1059-1069, 2010.
DOI : 10.1016/j.neuroimage.2009.10.003

N. Wiener, The theory of prediction, Modern mathematics for engineers 1, pp.125-139, 1956.

C. W. Granger, Investigating Causal Relations by Econometric Models and Cross-Spectral Methods, Econometrica: Journal of the Econometric Society, pp.424-438, 1969.
DOI : 10.1017/CBO9780511753978.002

Y. Saito and H. Harashima, Tracking of information within multichannel EEG record causal analysis in EEG Recent advances in EEG and EMG data processing, pp.133-146, 1981.

J. Geweke, Measurement of Linear Dependence and Feedback between Multiple Time Series, Journal of the American Statistical Association, vol.54, issue.378, pp.304-313, 1982.
DOI : 10.1080/01621459.1982.10477803

K. J. Blinowska, R. Ku´sku´s, and M. Kami´nskikami´nski, Granger causality and information flow in multivariate processes, Physical Review E, vol.70, issue.5, p.50902, 2004.
DOI : 10.1103/PhysRevE.70.050902

T. Schreiber, Measuring information transfer, Physical review letters, p.461, 2000.

A. Razi and K. J. Friston, The Connected Brain: Causality, models, and intrinsic dynamics, IEEE Signal Processing Magazine, vol.33, issue.3, pp.14-35, 2016.
DOI : 10.1109/MSP.2015.2482121

URL : http://arxiv.org/abs/1602.02945

K. J. Friston, L. Harrison, and W. Penny, Dynamic causal modelling, NeuroImage, vol.19, issue.4, pp.1273-1302, 2003.
DOI : 10.1016/S1053-8119(03)00202-7

URL : https://hal.archives-ouvertes.fr/inserm-00388972

J. Daunizeau, K. E. Stephan, and K. J. Friston, Stochastic dynamic causal modelling of fMRI data: Should we care about neural noise?, NeuroImage, vol.62, issue.1, pp.464-81, 2012.
DOI : 10.1016/j.neuroimage.2012.04.061

R. Moran, D. A. Pinotsis, and K. Friston, Neural masses and fields in dynamic causal modeling, Neural Masses and Fields: Modelling the, Dynamics of Brain Activity, 0190.

K. J. Friston, A. Bastos, V. Litvak, K. E. Stephan, P. Fries et al., DCM for complex-valued data: Cross-spectra, coherence and phase-delays, NeuroImage, vol.59, issue.1, pp.439-55, 2012.
DOI : 10.1016/j.neuroimage.2011.07.048

URL : http://doi.org/10.1016/j.neuroimage.2011.07.048

. Friston, A neural mass model of spectral responses in electrophysiology, Neuroimage, vol.37, issue.3, pp.706-726, 2007.

R. J. Moran, K. E. Stephan, T. Seidenbecher, H. C. Pape, R. J. Dolan et al., Dynamic causal models of steady-state responses, NeuroImage, vol.44, issue.3, pp.796-811, 2009.
DOI : 10.1016/j.neuroimage.2008.09.048

W. Xiang, C. Yang, J. Bellanger, H. Shu, and R. L. Jeannès, Inferring effective connectivity in epilepsy using dynamic causal modeling, IRBM, vol.36, issue.6, pp.335-344, 2015.
DOI : 10.1016/j.irbm.2015.09.001

URL : https://hal.archives-ouvertes.fr/hal-01260652

D. Friston and . Marinazzo, Tracking slow modulations in synaptic gain using dynamic causal modelling: validation in epilepsy, NeuroImage, vol.107, pp.117-126, 2015.

G. K. Cooray, B. Sengupta, P. K. Douglas, and K. J. Friston, Dynamic causal modelling of electrographic seizure activity using Bayesian belief updating, NeuroImage, vol.125, pp.1142-1154, 2016.
DOI : 10.1016/j.neuroimage.2015.07.063

J. Daunizeau, K. J. Friston, and S. J. Kiebel, Variational Bayesian identification and prediction of stochastic nonlinear dynamic causal models, Physica D: Nonlinear Phenomena, vol.238, issue.21, pp.2089-2118, 2009.
DOI : 10.1016/j.physd.2009.08.002

URL : http://doi.org/10.1016/j.physd.2009.08.002

K. Friston, J. Mattout, N. Trujillo-barreto, J. Ashburner, and W. Penny, Variational free energy and the Laplace approximation, Variational free energy and the laplace approximation, pp.220-254, 2007.
DOI : 10.1016/j.neuroimage.2006.08.035

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.160.2088

N. Ueda and R. Nakano, Deterministic annealing EM algorithm, Neural Networks, vol.11, issue.2, pp.271-282, 1998.
DOI : 10.1016/S0893-6080(97)00133-0

K. Katahira, K. Watanabe, and M. Okada, Deterministic annealing variant of variational Bayes method, Journal of Physics: Conference Series, vol.95, issue.1, p.12015, 2008.
DOI : 10.1088/1742-6596/95/1/012015

B. Sengupta, K. J. Friston, and W. D. Penny, Gradient-based MCMC samplers for dynamic causal modelling, NeuroImage, vol.125, pp.1107-1118, 2016.
DOI : 10.1016/j.neuroimage.2015.07.043

URL : http://doi.org/10.1016/j.neuroimage.2015.07.043

F. Wendling, A. Hernandez, J. Bellanger, P. Chauvel, and F. Bartolomei, Interictal to ictal transition in human temporal lobe epilepsy: insights from a computational model of intracerebral eeg, Journal of Clinical Neurophysiology, vol.22, issue.343, 2005.
URL : https://hal.archives-ouvertes.fr/inserm-00147326

M. Ursino, F. Cona, and M. Zavaglia, The generation of rhythms within a cortical region: Analysis of a neural mass model, NeuroImage, vol.52, issue.3, pp.1080-1094, 2010.
DOI : 10.1016/j.neuroimage.2009.12.084