download it. MEEG gathers functions from EEGLAB and other MATLAB-based open source frameworks to read/write and process MEG or simultaneous MEG/EEG (MEEG) data. Here we show a first decomposition by independent component analysis (ICA) of an MEEG data set and use MEEG plotting tools to localize

Recovering electrical activity of the brain from MEG/EEG measurements is known as the MEEG inverse problem. It is an ill-posed problem in several senses. One is that there is further less data observed than data to recover. One way to address this issue is to search for regular solutions. We

estimation by using EEG is not good comparing with that using MEG, because the human head consists of many different electric conductivity tissues such as brain, skull and scalp and it is difficult to know the electrical conductivity exactly. We developed a MEG/EEG hybrid method for source localization of a

We have developed two algorithms for source imaging from MEG/EEG data. Contribution to sensor data from a source at a particular voxel is expressed as the product of a known lead field and temporal basis functions with unknown coefficients. Temporal basis functions are in turn estimated from data

Although MEG/EEG signals are highly variable, systematic changes in distinct frequency bands are commonly encountered. These frequency-specific changes represent robust neural correlates of cognitive or perceptual processes (for example, alpha rhythms emerge on closing the eyes). However

Different modeling frameworks (such as error analyses for dipole localization [Fuchs, 1998; Huizenga, 2001]; crosstalk and point spread analyses for linear estimators [Liu, 2002]; etc.) have demonstrated improved three-dimensional (3D) resolution for combined MEG/EEG (or EMEG) source estimation

Inverse MEG/EEG problem is known to be ill-posed and no single solution can be found without utilizing some prior knowledge about the nature of signal sources, the way the signals are propagating and finally collected by the sensors. The signals are assumed to have a sparse representation

A new method based on a multiresolution approach for solving the ill-posed problem of brain electrical activity reconstruction from electroencephaloram (EEG)/magnetoencephalogram (MEG) signals is proposed in a distributed source model. At each step of the algorithm, a regularized solution

Cortical responses, recorded by electroencephalography and magnetoencephalography, can be characterized in the time domain, to study event-related potentials/fields, or in the time – frequency domain, to study oscillatory activity. In the literature, there is a common conception that evoked, induced, and on-going oscillations reflect different neuronal processes and mechanisms. In this work, we consider the relationship between the mechanisms generating neuronal transients and how they are expressed in terms of evoked and induced power. This relationship is addressed using a neuronally realistic model of interacting neuronal subpopulations. Neuronal transients were generated by changing neuronal input (a dynamic mechanism) or by perturbing the systems coupling parameters (a structural mechanism) to produce induced responses. By applying conventional time – frequency analyses, we show that, in contradistinction to common conceptions, induced and evoked oscillations are perhaps more related than previously reported. Specifically, structural mechanisms normally associated with induced responses can be expressed in evoked power. Conversely, dynamic mechanisms posited for evoked responses can induce responses, if there is variation in neuronal input. We conclude, it may be better to consider evoked responses as the results of mixed dynamic and structural effects. We introduce adjusted power to complement induced power. Adjusted power is unaffected by trial-totrial variations in input and can be attributed to structural perturbations without ambiguity. D 2006 Elsevier Inc. All rights reserved.

Seule la possibilit de localiser les sources des signaux MEG et EEG permettra de rpondre la question ÇÊquand et oÊÈ se droulent les tapes du traitement de lÕinformation dans le cerveau [Wood, 1994]. En effet, il est souvent frquent de dtecter en TEP ou en IRMf les mmes zones dÕactivation pour