MESURE DE COUPLAGE STATISTIQUE ENTRE SIGNAUX EEG : APPLICATION A L'EVALUATION QUANTITATIVE DES RELATIONS FONCTIONNELLES ENTRE STRUCTURES CEREBRALES EN EPILEPSIE: MESURE DE COUPLAGE STATISTIQUE ENTRE SIGNAUX EEG : APPLICATION A L'EVALUATION QUANTITATIVE DES RELATIONS FONCTIONNELLES ENTRE STRUCTURES CEREBRALES EN EPILEPSIE

Cerebral functional connectivity can be characterized by the temporal evolution of the correlation between signals recorded in spatially distributed brain areas. In this thesis, we propose a comprehensive and quantitative comparison for evaluating the performances of various classes of methods aimed at estimating this connectivity. Based on various simulation models, results show that the performances are strongly dependent on the characteristics of signals, i.e. none of the methods outperforms the others in all situations. Considering the non-stationary and oscillatory nature of the activity of neuronal populations, we propose a time-frequency (TF) estimator of the relationship for non-stationary signals. The objective comparison of this new estimator with a more classical one, based on the coherence function, shows that it can lead to better performances. On real data, results indicate that this estimator can also increase the readability of the TF representation of the relationship and can thus improve the interpretation of the interdependences between EEG signals.La connectivité fonctionnelle cérébrale peut être caractérisée par l'évolution temporelle de la corrélation entre les signaux enregistrés dans des régions spatialement distribuées. Ici, nous proposons une comparaison exhaustive et quantitative pour juger des performances de différentes classes de méthodes pour l'estimation de cette connectivité. Basés sur plusieurs modèles de simulation, les résultats montrent que les performances sont fortement dépendantes des caractéristiques des signaux, aucune méthode ne surpassant les autres dans toutes les situations. La nature non stationnaire et oscillatoire des activités des populations neuronales, nous a amené à proposer un estimateur Temps-Fréquence de relation. La comparaison objective de ce nouvel estimateur avec un estimateur plus classique, basé sur la fonction de cohérence, montre qu'il peut conduire à de meilleures performances. Sur des données réelles, les résultats indiquent que cet estimateur peut augmenter la lisibilité de la représentation TF de la relation et peut ainsi améliorer l'interprétation des relations entre signaux EEG

From EEG Signals to Brain Connectivity: Methods and Applications in Epilepsy.

During the past decades, considerable effort has been devoted to the development of signal processing techniques aimed at quantifying the temporal evolution of the cross-correlation (in a wide sense) between signals recorded from spatially-distributed regions in order to characterize brain functional connectivity during normal or pathological (as in epilepsy) conditions. Besides linear methods introduced in the field of EEG analysis fifty years ago, a number of studies have been dedicated to the development of nonlinear methods, mostly because of the nonlinear nature of mechanisms at the origin of EEG signals. Recent studies showed the potential value of methods commonly used in nonlinear physics (see Chap. 15). Three families of methods (linear and nonlinear regression, phase synchronization, and generalized synchronization) are reviewed. Their performances are evaluated on the basis of a simulation model in which a coupling parameter can be tuned between populations of neurons generating bivariate EEG time-series. This evaluation is performed according to quantitative criteria. The main findings of this evaluation are the following. First, some of the methods are insensitive to the coupling parameter. Second, results were found to be dependent on signal properties. In particular, the broadening of the frequency band is a parameter that strongly influences the performances. Third, and generally speaking, there is no 'universal' method for measuring statistical couplings among signals. Indeed, none of the studied methods performs better than the other ones for the two studied situations (background and epileptic activity). Finally, linear and nonlinear regression methods were found to be sensitive to the coupling parameter in all situations and showed either average or good performances. This latter point leads the authors to conclude that these "robust" methods should be applied before using more sophisticated methods

Valence-arousal evaluation using physiological signals in an emotion recall paradigm

The work presented in this paper aims at assessing human emotions using peripheral as well as electroencephalographic (EEG) physiological signals. Three specific areas of the valence-arousal emotional space are defined, corresponding to negatively excited, positively excited, and calm-neutral states. An acquisition protocol based on the recall of past emotional events has been designed to acquire data from both peripheral and EEG signals. Pattern classification is used to distinguish between the three areas of the valence-arousal space. The performance of two classifiers has been evaluated on different features sets: peripheral data, EEG data, and EEG data with prior feature selection. Comparison of results obtained using either peripheral or EEG signals confirms the interest of using EEG's to assess valence and arousal in emotion recall conditions

From EEG signals to brain connectivity: a model-based evaluation of interdependence measures.

International audienceIn the past, considerable effort has been devoted to the development of signal processing techniques aimed at characterizing brain connectivity from signals recorded from spatially-distributed regions during normal or pathological conditions. In this paper, three families of methods (linear and nonlinear regression, phase synchronization, and generalized synchronization) are reviewed. Their performances were evaluated according to a model-based methodology in which a priori knowledge about the underlying relationship between systems that generate output signals is available. This approach allowed us to relate the interdependence measures computed by connectivity methods to the actual values of the coupling parameter explicitly represented in various models of signal generation. Results showed that: (i) some of the methods were insensitive to the coupling parameter; (ii) results were dependent on signal properties (broad band versus narrow band); (iii) there was no "ideal" method, i.e., none of the methods performed better than the other ones in all studied situations. Nevertheless, regression methods showed sensitivity to the coupling parameter in all tested models with average or good performances. Therefore, it is advised to first apply these "robust" methods in order to characterize brain connectivity before using more sophisticated methods that require specific assumptions about the underlying model of relationship. In all cases, it is recommended to compare the results obtained from different connectivity methods to get more reliable interpretation of measured quantities with respect to underlying coupling. In addition, time-frequency methods are also recommended when coupling in specific frequency sub-bands ("frequency-locking") is likely to occur as in epilepsy

Dynamic routing and wavelength assignment: Artificial bee colony optimization

In this paper a novel approach for modelling Routing and Wavelength Assignment (RWA) problem in wavelength-routed Dense Wavelength Division Multiplexing (DWDM) optical networks is proposed. A new idea based on Artificial Bee Colony (ABC) algorithm is introduced for solving RWA problem which is known to be an NP-hard problem. In the proposed ABC-RWA approach every food source represents a possible and feasible lightpath between each original and destination node pair in demand matrix. The positions of food sources are modified by some artificial bees in the population where the aim is to discover the places of food sources. The food source with the highest nectar value seems to be a solution which is evaluated by the fitness function. The simulation results demonstrate the ability and efficiency of proposed approach for solving RWA in real-world optical networks. The proposed approach could be extended for dynamic RWA schemes in real-time applications and employed by network resilience architectures

Quantitative evaluation of linear and nonlinear methods characterizing interdependencies between brain signals.

International audienceBrain functional connectivity can be characterized by the temporal evolution of correlation between signals recorded from spatially-distributed regions. It is aimed at explaining how different brain areas interact within networks involved during normal (as in cognitive tasks) or pathological (as in epilepsy) situations. Numerous techniques were introduced for assessing this connectivity. Recently, some efforts were made to compare methods performances but mainly qualitatively and for a special application. In this paper, we go further and propose a comprehensive comparison of different classes of methods (linear and nonlinear regressions, phase synchronization, and generalized synchronization) based on various simulation models. For this purpose, quantitative criteria are used: in addition to mean square error under null hypothesis (independence between two signals) and mean variance computed over all values of coupling degree in each model, we provide a criterion for comparing performances. Results show that the performances of the compared methods are highly dependent on the hypothesis regarding the underlying model for the generation of the signals. Moreover, none of them outperforms the others in all cases and the performance hierarchy is model dependent

Comparison of two estimators of time-frequency interdependencies between nonstationary signals: application to epileptic EEG.

International audienceNumerous works have been dedicated to the development of signal processing methods aimed at measuring the degree of association between EEG signals. This interdependency parameter is often used to characterize the functional coupling between different brain structures or regions during either normal or pathological processes. In this paper we focus on the time-frequency characterization of interdependencies between nonstationary signals. Particularly, we propose a novel estimator based on the cross correlation of narrow band filtered signals. In a simulation framework, results show that this estimator may exhibit higher statistical performances (bias and variance) compared to a more classical estimator based on the coherence function. On real data (intracerebral EEG signals), they show that this estimator enhances the readability of the time-frequency representation of the relationship and can thus improve the interpretation of nonstationary interdependencies in EEG signals. Finally, we illustrate the importance of characterizing the relationship in both time and frequency domains by comparing with frequency-independent methods (linear and nonlinear)