R. Le Bouquin Jeannes

Analysis of auditory evoked potential parameters in the presence of radiofrequency fields using a support vector machines method.

The paper presents a study of global system for mobile (GSM) phone radio-frequency effects on human cerebral activity. The work was based on the study of auditory evoked potentials (AEPs) recorded from healthy humans and epileptic patients. The protocol allowed the comparison of AEPs recorded with or without exposure to electrical fields. Ten variables measured from AEPs were employed in the design of a supervised support vector machines classifier. The classification performance measured the classifier′s ability to discriminate features performed with or without radiofrequency exposure. Most significant features were chosen by a backward sequential selection that ranked the variables according to their pertinence for the discrimination. Finally, the most discriminating features were analysed statistically by a Wilcoxon signed rank test. For both populations, the N100 amplitudes were reduced under the influence of GSM radiofrequency (mean attenuation of −0.36μV for healthy subjects and −0.6OμV for epileptic patients). Healthy subjects showed a NIOO latency decrease (−5.23ms in mean), which could be consistent with mild, localised heating. The auditory cortical activity in humans was modified by GSM phone radio-frequencies, but an effect on brain functionality has not been proven.

How to improve acoustic echo and noise cancelling using a single talk detector

Abstract This paper deals with speech enhancement for hands-free audio terminals, including two major problems: noise reduction and acoustic echo cancellation. Our objective is to combine a noise reduction system and an acoustic echo canceller to get a near-end speech signal with a minimum distortion and low levels of echo and noise. We present four structures (using one or two microphones and one loudspeaker) where the operation of echo cancellation comes before that of noise reduction. Generally, the noise reduction system is derived from the output of the acoustic echo canceller. An alternative is to derive the noise reduction directly from the microphone observation in order to decrease the distortion on the near-end speech signal. Experimental results are presented. Finally, in the mono-channel situation, an optimized structure controlled by an echo detector is proposed and tested.

Quantifying connectivity in a physiology based model using adaptive dynamic causal modelling

This paper proposes an Adaptive Dynamic Causal Modelling based approach to detect and quantify effective connectivity in human brain structures injured by epileptic activities. The identification of the parameters in the physiology based model subtended the Electroencephalographic observations is performed by improving the optimization step in the Expectation Maximization algorithm. Considering unidirectional flow propagation, we show the efficiency of our proposed approach compared to the conventional technique.

Caractérisation de l’activité cérébrale auditive recueillie en champ électromagnétique émis par des téléphones portables

Resume Cet article traite de l’influence des champs radio-electriques emis par les telephones portables sur l’activite cerebrale humaine. Notre travail est realise sur le systeme auditif a partir du recueil de potentiels evoques auditifs (PEA) a la surface du scalp de sujets sains et de patients epileptiques. Le protocole permet de comparer les PEA enregistres avec ou sans exposition aux radiofrequences. Les stimuli sont deux sons purs et un simulateur de communication permet de controler la puissance des radiofrequences emises. Pour obtenir une reference et tenir compte du phenomene d’accoutumance, une experience fictive sans emission de radiofrequences, dite seance placebo, est egalement consideree. Notre etude consiste a mettre en œuvre des mesures fiables qui soient revelatrices de certains changements ou de certaines constances dans les signaux enregistres. Elles sont fondees sur les amplitudes et les latences de l’onde N100, les coefficients de correlation calcules entre signaux moyens, ainsi que ceux calcules entre les spectres de ces signaux. Les mesures proposees et fondees sur les parametres precedents prennent en compte le phenomene d’accoutumance. Les variations des amplitudes et des latences de l’onde N100 mettent en evidence un dysfonctionnement cognitif attribuable aux radiofrequences. Une diminution des latences chez les sujets sains peut etre due a une meilleure synchronisation neuronale ou a un effet thermique. Chez les patients epileptiques, un allongement de ces latences peut etre imputable a une alteration de la voie afferente. Une difference entre les coefficients de correlation calcules entre signaux ainsi qu’une difference entre les coefficients de correlation calcules entre les spectres des signaux placebo et experimentaux traduisent un effet des radiofrequences qui induit une desorganisation neuronale dont l’incidence reelle sur l’activite cerebrale n’est pas prouvee.

Investigation of nonlinear granger causality in the context of epilepsy

Granger causality approaches have been widely used to estimate effective connectivity in complex dynamic systems. These techniques are based on the building of predictive models which not only depend on a proper selection of the predictive vectors size but also on the chosen class of regression functions. The question addressed in this paper is the estimation of the model order in the computation of Granger causality indices to characterize the propagation flow between simulated epileptic signals. In this contribution, a new strategy is proposed to select a suitable model order for potentially nonlinear systems. A nonlinear vectorial autoregressive model based on a wavelet network is considered for the identification and an optimal nonlinear model order is selected using the Bayesian information criterion and imported in nonlinear kernel predictors to derive Granger causality. Simulations are firstly conducted on a linear autoregressive model, then on toy nonlinear models and, finally, on simulated intracranial electroencephalographic signals obtained from an electrophysiology based model to reveal the directional relationships between time series data. The performance of our approach proves the effectiveness of the new strategy in the Granger index estimation.

Comparison of granger causality measures to detect effective connectivity in the context of epilepsy

Effective connectivity can be modeled and quantified with a number of techniques. The aim of this study is to reveal the direction of the information flow and to quantify the magnitude of coupling between epileptic brain structures using Granger Causality (GC) approaches. Since traditional linear GC cannot identify non-linear effects in the data, the non-linear extension of this measure is recommended. A comparative study between linear and non-linear GC is performed to determine the importance of the non-linear measure in the study of complex dynamical systems as neural networks. Experiments are first conducted on a linear autoregressive model, then on a non-linear model and finally on a model of intracranial EEG signals generation before giving some conclusions on the relevance on the different indices.