SEEG dipole source localization based on an empirical Bayesian approach taking into account forward model uncertainties.

Electromagnetic brain source localization consists in the inversion of a forward model based on a limited number of potential measurements. A wide range of methods has been developed to regularize this severely ill-posed problem and to reduce the solution space, imposing spatial smoothness, anatomical constraint or sparsity of the activated source map. This last criteria, based on physiological assumptions stating that in some particular events (e.g., epileptic spikes, evoked potential) few focal area of the brain are simultaneously actives, has gained more and more interest. Bayesian approaches have the ability to provide sparse solutions under adequate parametrization, and bring a convenient framework for the introduction of priors in the form of probabilistic density functions. However the quality of the forward model is rarely questioned while this parameter has undoubtedly a great influence on the solution. Its construction suffers from numerous approximation and uncertainties, even when using realistic numerical models. In addition, it often encodes a coarse sampling of the continuous solution space due to the computational burden its inversion implies. In this work we propose an empirical Bayesian approach to take into account the uncertainties of the forward model by allowing constrained variations around a prior physical model, in the particular context of SEEG measurements. We demonstrate on simulations that the method enhance the accuracy of the source time-course estimation as well as the sparsity of the resulting source map. Results on real signals prove the applicability of the method in real contexts.

Evaluating dipolar source localization feasibility from intracerebral SEEG recordings.

Stereo-electroencephalography (SEEG) is considered as the golden standard for exploring targeted structures during pre-surgical evaluation in drug-resistant partial epilepsy. The depth electrodes, inserted in the brain, consist of several collinear measuring contacts (sensors). Clinical routine analysis of SEEG signals is performed on bipolar montage, providing a focal view of the explored structures, thus eliminating activities of distant sources that propagate through the brain volume. We propose in this paper to exploit the common reference SEEG signals. In this case, the volume propagation information is preserved and electrical source localization (ESL) approaches can be proposed. Current ESL approaches used to localize and estimate the activity of the neural generators are mainly based on surface EEG/MEG signals, but very few studies exist on real SEEG recordings, and the case of equivalent current dipole source localization has not been explored yet in this context. In this study, we investigate the influence of volume conduction model, spatial configuration of SEEG sensors and level of noise on the ESL accuracy, using a realistic simulation setup. Localizations on real SEEG signals recorded during intracerebral electrical stimulations (ICS, known sources) as well as on epileptic interictal spikes are carried out. Our results show that, under certain conditions, a straightforward approach based on an equivalent current dipole model for the source and on simple analytical volume conduction models yields sufficiently precise solutions (below 10mm) of the localization problem. Thus, electrical source imaging using SEEG signals is a promising tool for distant brain source investigation and might be used as a complement to routine visual interpretations.

Automatic lateralization of temporal lobe epilepsy based on scalp EEG.

OBJECTIVE: The objective of this work is the determination of the lateralization of the epileptic seizure onset zone using the scalp EEG signal processing. METHODS: A comprehensive method based on the evaluation of the evolution of the correlation coefficients computed between bipolar channels (longitudinal montage) was applied to 43 patients (87 seizures). The correlation coefficients are estimated by a nonlinear regression analysis. The methodology that leads to the lateralization is based on several processing steps: segmentation, seizure onset determination and then lateralization. RESULTS: Results show that the mean level of the nonlinear correlation values computed between EEG channels at the seizure onset time is significantly higher on the side of the beginning of a seizure. CONCLUSIONS: The side of the seizure onset was determined for about 80-90% of the seizures studied with a satisfactory high reproducibility level. SIGNIFICANCE: Comparison of nonlinear correlation coefficients between both sides of the brain leads to the determination of the side of seizure onset.

EEG preprocessing for synchronization estimation and epilepsy lateralization.

The global framework of this paper is the synchronization analysis in EEG recordings. Two main objectives are pursued: the evaluation of the synchronization estimation for lateralization purposes in epileptic EEGs and the evaluation of the effect of the preprocessing (artifact and noise cancelling by blind source separation, wavelet denoising and classification) on the synchronization analysis. We propose a new global synchronization index, based on the classical cross power spectrum, estimated for each cerebral hemisphere. After preprocessing, the proposed index is able to correctly lateralize the epileptic zone in over 90% of the cases.

Reference estimation in EEG recordings.

This work aims to analyze the reference (montage) problem in electroencephalographic (EEG) recordings. It is well accepted that EEG signals are a mixture of cerebral and extracerebral sources, and the solution to the reference problem depends on the hypothesized mixing model. We focus here on an acquisition model using a distant reference electrode and propose a method for determining and eliminating the reference signal which develops and improves Hu et al. work from [1]. The obtained solution, based on a constrained blind source separation (BSS) algorithm, outperforms the cited method on simulated noisy EEG signals for all noise levels.

Analysis of the relationship between interictal electrical source imaging and PET hypometabolism.

The aim of this paper is to compare interictal EEG source localizations with statistical analysis of hypometabolisms in PET brain imaging. Both methods are currently used in the pre-surgical evaluation of drug-resistant partial epilepsy, but the relationship between electrical source localizations and hypometabolic areas has not been well defined yet. At the present time, these two methods have been performed on five patients in order to develop a comparative quantitative study with these first results which should be then extended to a larger patient database.

Influence of source separation and montage on ictal source localization.

The aim of this paper is to evaluate the influence of the reference electrode (introduced to form an augmented average montage) and of the artifact elimination by blind source separation on the ictal electrical source imaging. We present here a preliminary study on one patient only. The results seem to indicate that the montage (and thus the reference handling method) has a limited but existent influence on the quality of the source localization. Artifact elimination highly improves this quality as well.

Combined SEEG and source localisation study of temporal lobe schizencephaly and polymicrogyria.

OBJECTIVES: Type 1 schizencephaly (SZ) is a cerebral malformation characterised by a cleft lined and surrounded by a polymicrogyric cortex, extending from the pial region to the peri-ventricular heterotopia. Our purpose was to combine and compare dipole source imaging technique and Stereo-EEG (SEEG) technique in determining the irritative and epileptogenic zones in a case of type 1 schizencephaly. METHODS: High-resolution (64-channel) video-EEG with electrical source imaging and SEEG recordings were performed during a pre-surgical evaluation for medically intractable epilepsy. RESULTS: Anatomo-electro-clinical correlations based on SEEG and source localisation identified two irritative and epileptogenic zones partially overlapping the polymicrogyric cortex surrounding the SZ: an anterior medio-lateral network primarily involving dysplasic limbic structures and a lateral network involving the anterior and middle part of the cleft and polymicrogyric cortex. The most posterior part (at the temporo-parieto-occipital junction) displayed a normal background activity. CONCLUSIONS: Both epileptogenic and electrophysiologically normal cortices coexisted within the same widespread malformation: only the anterior part belonged to the anterior medio-lateral epileptogenic network defined by the SEEG. SIGNIFICANCE: In cases of widespread cortical malformation such as SZ, source localization techniques can help to define the irritative zone and relevant targets for SEEG.

EEG ocular artefacts and noise removal.

The general framework of this research is the pre-processing of the electroencephalographic (EEG) signals. The goal of this paper is to compare several combinations of wavelet denoising (WD) and independent component analysis (ICA) algorithms for noise and artefacts removal. These methods are tested on simulated EEG, using different evaluation criteria. According to our results, the most effective method consists in source separation by SOBI-RO [1], followed by wavelet denoising by SURE thresholding [2].

Linear dichroism of the retinal nerve fiber layer expressed with Mueller matrices.

An electro-optic device is used that permits the measurement of polarized absorption spectra (linear dichroism). The change of the polarization state of a light beam brought about by passage through the optic elements of a dichrograph are described mathematically by a transformation of the Stokes vector. The polarization or absorption properties of the optical elements are described by the Mueller matrices. The dichroic properties of sheep retina and cornea are studied in vitro.

Principal component analysis and interpretation of bowel sounds.

This paper presents a method for abdominal sounds analysis based on principal component analysis (PCA). The first steps (wavelet denoising and segmentation, followed by spatial localization) were presented in previous works. After extracting physical features (activity indices) from long time six channel recordings, we propose a reduced representation space obtained by PCA and we present our results in phonoenterogram analysis.