Localization of extended brain sources from EEG/MEG: The ExSo-MUSIC approach.

International audienceWe propose a new MUSIC-like method, called 2q-ExSo-MUSIC (q≥1). This method is an extension of the 2q-MUSIC (q≥1) approach for solving the EEG/MEG inverse problem, when spatially-extended neocortical sources ("ExSo") are considered. It introduces a novel ExSo-MUSIC principle. The novelty is two-fold: i) the parameterization of the spatial source distribution that leads to an appropriate metric in the context of distributed brain sources and ii) the introduction of an original, efficient and low-cost way of optimizing this metric. In 2q-ExSo-MUSIC, the possible use of higher order statistics (q≥2) offers a better robustness with respect to Gaussian noise of unknown spatial coherence and modeling errors. As a result we reduced the penalizing effects of both the background cerebral activity that can be seen as a Gaussian and spatially correlated noise, and the modeling errors induced by the non-exact resolution of the forward problem. Computer results on simulated EEG signals obtained with physiologically-relevant models of both the sources and the volume conductor show a highly increased performance of our 2q-ExSo-MUSIC method as compared to the classical 2q-MUSIC algorithms

Seizure onset zone localization from ictal high-density EEG in five patients

Rationale Because epilepsy is a network disease, localization of the exact seizure onset zone (SOZ) is difficult because the epileptic activity can spread to other regions within milliseconds. Functional connectivity metrics quantify how the activity in different brain regions is interrelated. In the past, it has been shown that functional connectivity analysis of ictal intracranial EEG (icEEG) recordings can help with SOZ localization in patients with focal epilepsy (van Mierlo et al., 2014). However, it would be of high clinical value to be able to localize the SOZ based on non-invasive ictal EEG recordings to optimize the icEEG implantation scheme or to avoid invasive monitoring and improve surgical outcome. In this work, we propose an approach to localize the SOZ based on non-invasive ictal high-density EEG (hd-EEG) recordings. Methods We considered retrospective ictal epochs of 2.4 s up to 10 s recorded with hd-EEG (256 electrodes) in five patients who were rendered seizure free after surgery. From the 256 electrodes, the facial electrodes were removed, resulting in a subset of 204 electrodes. A 28-channel subset was constructed to mimic a low-density (ld) electrode setup used in clinical practice. EEG source imaging (ESI) was performed in the CARTOOL software using an individual head model (LSMAC) to calculate the forward model (Brunet et al., 2011). We considered sources uniformly distributed in the brain with a spacing of 5 mm. LORETA (Pascal-Marqui et al., 1994) was used as inverse solution method. In each cluster of activity, we determined a central source based on the criterion that there was no higher power in its neighborhood. The time-varying connectivity pattern between the time series of these sources was calculated using Granger causality (van Mierlo et al., 2013). This was done in the frequency band containing the fundamental seizure frequency, 3-30Hz. The outdegree of each selected dipole was determined as the sum over time of all outgoing connections. Around the dipole with the highest outdegree, we determined a region of dipoles that had a power that was at least 90% of the power of the center dipole. This region was then considered as the SOZ. Results We were able to successfully localize the driver in the resected zone for all patients based on ESI followed by connectivity analysis of the hd-EEG (mean localization error (LE) = 0 mm). If we chose the cluster with the highest power as driver, the mean LE was 59.69 mm. For the ld-EEG, ESI followed by connectivity analysis resulted in a mean LE of 23.30 mm and when selecting the cluster with the highest power as driver, the mean LE was 31.21 mm. Conclusions ESI in combination with connectivity analysis can successfully localize the SOZ in non-invasive ictal hd-EEG recordings and greatly outperforms localization based on power. For ld-EEG recordings, the localization error remains significant but still outperforms localization based on power. This could have important clinical relevance for the presurgical evaluation in focal epilepsy

Blind Source Separation Methods Applied to Muscle Artefacts Removing from Epileptic Eeg Recording: A Comparative Study.

International audienceElectroencephalogram (EEG) recordings are often contaminated with muscle artifacts. These artifacts obscure the EEG and complicate its interpretation or even make the interpretation unfeasible. In this paper, realistic spike EEG signals are simulated from the activation of a 5 cm2 epileptic patch in the left superior temporal gyrus. Background activities and real muscle artifacts are then added to the simulated data. We compare the efficiency of Empirical Mode Decomposition (EMD), Independent Component Analysis (ICA) and Blind Source Separation based on Canonical Correlation Analysis (BSS-CCA) to remove muscle artifacts from the EEG signals. The quantitative comparison indicates that the EMD approach exhibits a better performance than ICA and BSS-CCA, especially in the case of very low Signal to Noise Ratio (SNR)

From oscillatory transcranial current stimulation to scalp EEG changes: a biophysical and physiological modeling study.

International audienceBoth biophysical and neurophysiological aspects need to be considered to assess the impact of electric fields induced by transcranial current stimulation (tCS) on the cerebral cortex and the subsequent effects occurring on scalp EEG. The objective of this work was to elaborate a global model allowing for the simulation of scalp EEG signals under tCS. In our integrated modeling approach, realistic meshes of the head tissues and of the stimulation electrodes were first built to map the generated electric field distribution on the cortical surface. Secondly, source activities at various cortical macro-regions were generated by means of a computational model of neuronal populations. The model parameters were adjusted so that populations generated an oscillating activity around 10 Hz resembling typical EEG alpha activity. In order to account for tCS effects and following current biophysical models, the calculated component of the electric field normal to the cortex was used to locally influence the activity of neuronal populations. Lastly, EEG under both spontaneous and tACS-stimulated (transcranial sinunoidal tCS from 4 to 16 Hz) brain activity was simulated at the level of scalp electrodes by solving the forward problem in the aforementioned realistic head model. Under the 10 Hz-tACS condition, a significant increase in alpha power occurred in simulated scalp EEG signals as compared to the no-stimulation condition. This increase involved most channels bilaterally, was more pronounced on posterior electrodes and was only significant for tACS frequencies from 8 to 12 Hz. The immediate effects of tACS in the model agreed with the post-tACS results previously reported in real subjects. Moreover, additional information was also brought by the model at other electrode positions or stimulation frequency. This suggests that our modeling approach can be used to compare, interpret and predict changes occurring on EEG with respect to parameters used in specific stimulation configurations

EEG extended source localization: Tensor-based vs. conventional methods

International audienceThe localization of brain sources based on EEG measurements is a topic that has attracted a lot of attention in the last decades and many different source localization algorithms have been proposed. However, their performance is limited in the case of several simultaneously active brain regions and low signal-to-noise ratios. To overcome these problems, tensor-based preprocessing can be applied, which consists in constructing a space-time-frequency (STF) or space-time-wave-vector (STWV) tensor and decomposing it using the Canonical Polyadic (CP) decomposition. In this paper, we present a new algorithm for the accurate localization of extended sources based on the results of the tensor decomposition. Furthermore, we conduct a detailed study of the tensor-based preprocessing methods, including an analysis of their theoretical foundation, their computational complexity, and their performance for realistic simulated data in comparison to conventional source localization algorithms such as sLORETA, cortical LORETA (cLORETA), and 4-ExSo-MUSIC. Our objective consists, on the one hand, in demonstrating the gain in performance that can be achieved by tensor-based preprocessing, and, on the other hand, in pointing out the limits and drawbacks of this method. Finally, we validate the STF and STWV techniques on real measurements to demonstrate their usefulness for practical applications

Blind underdetermined mixture identification by joint canonical decomposition of HO cumulants

12 pages , in one part by the ANR DECOTES Contract (), in second part by the mv-EMD Contract() and in third part byInternational audienceA new family of cumulant-based algorithms is proposed in order to blindly identify potentially underdetermined mixtures of statistically independent sources. These algorithms perform a joint canonical decomposition (CAND) of several higher order cumulants through a CAND of a three-way array with special symmetries. These techniques are studied in terms of identifiability, performance and numerical complexity. From a signal processing viewpoint, the proposed methods are shown i) to have a better estimation resolution and ii) to be able to process more sources than the other classical cumulant-based techniques. Second, from a numerical analysis viewpoint, we deal with the convergence speed of several procedures for three-way array decomposition, such as the ACDC scheme. We also show how to accelerate the iterative CAND algorithms by using differently the symmetries of the considered three-way array. Next, from a multilinear algebra viewpoint the paper aims at giving some insights on the uniqueness of a joint CAND of several Hermitian multiway arrays compared to the CAND of only one array. This allows us, as a result, to extend the concept of virtual array (VA) to the case of combination of several VAs

Sequential High-Resolution Direction Finding From Higher Order Statistics

12 pagesInternational audienceThe classical higher order MUSIC-like methods based on a simultaneous search for all Directions Of Arrival (DOA's) show i) a capacity for processing underdetermined mixtures of sources, ii) a high robustness with respect to both a Gaussian noise with unknown spatial coherence and modeling errors, and iii) a better resolution than algorithms based on second order statistics. However, these methods have some limits: for a finite number of samples, they show poor performance for sources exhibiting quasi-collinear DOA's. In order to overcome this drawback, two new sequential MUSIC-like algorithms are proposed in this paper, namely the 2q-D-MUSIC and the 2q-RAP-MUSIC (q>1) algorithms. These methods are based on a sequential optimization of proposed generalized noise and signal 2q-MUSIC metrics, respectively. That allows us to learn and then to take into account the level of correlation between sources. A comparative study, both in terms of performance and numerical complexity, is performed showing the interest of the proposed techniques when some sources are angularly close. Eventually, an upper bound of the maximum number of sources which can be processed by the 2q-MUSIC-like techniques is given for all q. This improves recent work on the 2q-th order virtual arrays

Estimation paramétrique aux ordres supérieurs (application à la goniométrie et à la localisation de sources d'activités cérébrales)

Notre travail porte sur la conception et la validation de nouvelles méthodes de résolution de problèmes inverses rencontrés i) en télécommunications afin de rechercher les angles d'incidence d ondes radio reçues par une antenne et ii) en ingénierie biomédicale pour localiser des sources d activité électrique cérébrale à partir de mesures électromagnétiques sur le cuir chevelu. Les techniques proposées s'appuient sur la méthode MUSIC, en raison de son comportement asymptotique remarquable. De plus, à la manière de 2q-MUSIC, elles exploitent les statistiques d'ordre supérieur, insensibles à la présence d un bruit gaussien de cohérence spatiale inconnue, et tolérant un nombre de capteurs inférieur au nombre de sources ainsi que des erreurs dans le modèle de transfert entre la source et son observation. En outre, nos méthodes visent à améliorer le pouvoir séparateur de 2q-MUSIC ainsi que ses performances en présence de sources spatialement étendues. Nous proposons deux approches, 2q-D-MUSIC (q >= 1) et 2q-RAP-MUSIC (q >= 2), qui revisitent le principe de déflation d ordre 2 et l étendent aux ordres supérieurs. Dans le cas d'ondes radio angulairement proches, ces algorithmes augmentent la résolution et la capacité de détection de 2q-MUSIC, aussi bien pour des mélanges de sources sur- que sous-déterminés, qu'en présence ou non d'erreurs de modèle ou d'un bruit gaussien de cohérence spatiale inconnue. Nous proposons également la méthode 2q-ExSo-MUSIC (q >= 1) pour traiter des sources spatialement étendues. L étude de ses performances, sur des signaux EEG simulés réalistes, montre une meilleure localisation spatiale des sources d activités cérébrales que 2q-MUSIC.Three new methods are proposed and evaluated in this manuscript. The purpose of these methods is to solve inverse problems encountered in i) radiocommunications when searching the direction of arrival (DOA) of radio waves received by an antenna and ii) in biomedical engineering when localizing the sources of brain electrical activities from scalp electromagnetic recordings. The proposed techniques are based on the MUSIC algorithm, given its infinite asymptotic resolution. Moreover, as 2q-MUSIC our methods exploit higher order statistics in order to i) remain insensitive to Gaussian noise with unknown spatial coherence, ii) deal with a number of sensors lesser than the number of sources and iii) with errors induced when modeling the transfer between the source and the observation. Furthermore, our methods aim at increasing the separation ability of 2q-MUSIC and its performance in the presence of spatially extended sources. First, we propose two approaches referred to as 2q-D-MUSIC (q >= 1) and 2q-RAP-MUSIC (q >= 2), that use a new higher order deflation scheme. When radio waves present close angles of DOA, these algorithms increase the resolution and the detection capacity of 2q-MUSIC. This behavior is true in the case of both over- and underdetermined mixtures of sources, and is true in the presence of modeling errors or Gaussian noise of unknown spatial coherence. Second, we propose the 2q-ExSo-MUSIC (q >= 1) method in order to process brain extended sources. A study of its performance using realistic simulated EEG shows that 2q-ExSo-MUSIC is more accurate than 2q-MUSIC for the localization of brain extended sources.RENNES1-BU Sciences Philo (352382102) / SudocSudocFranceF

Localization of extended intracerebral current sources: Application to epilepsy.

International audienceWe propose a new method for localizing intracerebral current sources at the origin of epileptic spikes from non-invasive EEG/MEG data. This method was designed to account for three main constrains. First, most relevant spike generation models assume that sources are extended, i.e. spatially distributed over a focal or muti-focal area. Second, the background activity of the brain also contributes to EEG/MEG signals recorded during epileptic events. In this context, it can be seen as a penalizing Gaussian and spatially correlated noise. Third the array manifold is usually corrupted by errors due to the complexity of the conduction head volume. The proposed method is an adaptation of the well-established MUSIC method, that allows for the localization of Extended Sources (ExSo) assuming that all current dipoles comprised in the extended source are synchronous. In addition, we use Higher Order (HO) statistics, which are asymptotically insensitive to a Gaussian noise of unknown spatial coherence and which offer a greater robustness with respect to modeling errors. The method is called 2q-ExSo-MUSIC (q ges 2) as it combines the ExSo-MUSIC principle with the use of HO statistics. Using computer simulations of EEG signals, it is shown to highly increase the performance of classical MUSIC-like algorithms when physiologically relevant models for current sources and for volume conduction are considered