Reference-Based Source Separation Method For Identification of Brain Regions Involved in a Reference State From Intracerebral EEG

Samadi, Samareh; Amini, Ladan; Cosandier-Rimélé, D.; Soltanian-Zadeh, Hamid; Jutten, Christian

doi:10.1109/tbme.2013.2247401

Cited by 9 publications

(8 citation statements)

References 17 publications

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“…We find the source spatial map in four steps. First by using a referenced based source separation (R-SS) method (Samadi et al, 2013) the time courses of the HCTP sources are estimated, secondly the forward model is projected on the HCTP source space and thirdly, the spatial map of each HCTP source is estimated using a sparse decomposition method. Finally, Pareto optimization method is used to find the active regions.…”

Section: Methodsmentioning

confidence: 99%

“…We call them highly correlated with interested task paradigm sources (HCTP sources). The HCTP source space is found by applying the reference-based source separation (R-SS) method proposed in (Samadi et al, 2013). R-SS is a semi-blind source separation method which extracts the discriminating sources of two data groups, one group being related to the task of interest.…”

Section: Introductionmentioning

confidence: 99%

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Integrated Analysis of EEG and fMRI Using Sparsity of Spatial Maps

2016

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Integration of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is an open problem, which has motivated many researches. The most important challenge in EEG-fMRI integration is the unknown relationship between these two modalities. In this paper, we extract the same features (spatial map of neural activity) from both modality. Therefore, the proposed integration method does not need any assumption about the relationship of EEG and fMRI. We present a source localization method from scalp EEG signal using jointly fMRI analysis results as prior spatial information and source separation for providing temporal courses of sources of interest. The performance of the proposed method is evaluated quantitatively along with multiple sparse priors method and sparse Bayesian learning with the fMRI results as prior information. Localization bias and source distribution index are used to measure the performance of different localization approaches with or without a variety of fMRI-EEG mismatches on simulated realistic data. The method is also applied to experimental data of face perception of 16 subjects. Simulation results show that the proposed method is significantly stable against the noise with low localization bias. Although the existence of an extra region in the fMRI data enlarges localization bias, the proposed method outperforms the other methods. Conversely, a missed region in the fMRI data does not affect the localization bias of the common sources in the EEG-fMRI data. Results on experimental data are congruent with previous studies and produce clusters in the fusiform and occipital face areas (FFA and OFA, respectively). Moreover, it shows high stability in source localization against variations in different subjects.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrated Analysis of EEG and fMRI Using Sparsity of Spatial Maps

2016

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“…Indeed, we want to test if a priori information on the spike-like subspace can be used in the denoising process. For EEG signals, it has been previously shown that different assumptions about sources of interest, such as spatial constraints [14], locations of known sources [15], shape and latency of the signal of interest [16] and time support of spikes [17], can be considered in semi-blind or constrained source separation methods. In this paper, we use the timing information of the epileptic interictal sources (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…These preprocessing stages consist of the detection and clustering of the epileptic spikes involved in each source of interest. It should be mentioned that a GEVD-based method was previously proposed in [17] in order to determine epileptic regions from epileptic intracerebral EEG signals. This method has a manual preprocessing stage to extract periods including interictal epileptiform discharges.…”

Section: Introductionmentioning

confidence: 99%

Interictal EEG noise cancellation: GEVD and DSS based approaches versus ICA and DCCA based methods

Sardouie

Shamsollahi

Albera

et al. 2015

IRBM

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“…One of the main research topics on the application of ICA to EEG signals is the dynamic modeling of brain oscillations in humans [230,189,109,61] and lab rats [232,10]. This research attempts to combine the advantages of independent component analysis with the capabilities of certain dynamic models to deal with the temporal variability of the EEG.…”

Section: Application On Electroencephalographic Signalsmentioning

confidence: 99%

New Insights in Prediction and Dynamic Modeling from Non-Gaussian Mixture Processing Methods

Armero¹

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This thesis considers new applications of non-Gaussian mixtures in the framework of statistical signal processing and pattern recognition. The non-Gaussian mixtures were implemented by mixtures of independent component analyzers (ICA). The fundamental hypothesis of ICA is that the observed signals can be expressed as a linear transformation of a set of hidden variables, usually referred to as sources, which are statistically independent. This independence allows factoring the original M-dimensional probability density function (PDF) of the data as a product of one-dimensional probability densities, greatly simplifying the modeling of the data. ICA mixture models (ICAMM) provide further flexibility by alleviating the independency requirement of ICA, thus allowing the model to obtain local projections of the data without compromising its generalization capabilities. Here are explored new possibilities of ICAMM for the purposes of estimation and classification of signals.The thesis makes several contributions to the research in non-Gaussian mixtures: (i) a method for maximum-likelihood estimation of missing data, based on the maximization of the PDF of the data given the ICAMM; (ii) a method for Bayesian estimation of missing data that minimizes the mean squared error and can obtain the confidence interval of the prediction; (iii) a generalization of the sequential dependence model for ICAMM to semi-supervised or supervised learning and multiple chains of dependence, thus allowing the use of multimodal data; and (iv) introduction of ICAMM in diverse novel applications, both for estimation and for classification.The developed methods were validated via an extensive number of simulations that covered multiple scenarios. These tested the sensitivity of the proposed methods with respect to the following parameters: number of values to estimate; kinds of source distributions; correspondence of the data with respect to the assumptions of the model; number of classes in the mixture model; and unsupervised, semi-supervised, and supervised learning. The performance of the proposed methods was evaluated using several figures of merit, and compared with the performance of multiple classical and state-of-the-art techniques for estimation and classification.Aside from the simulations, the methods were also tested on several sets of real data from different types: data from seismic exploration studies; ground penetrating radar surveys; and biomedical data. These data correspond to the following applications: reconstruction of damaged or missing data from ground-penetrating radar surveys of historical walls; reconstruction of damaged or missing data from a seismic exploration survey; reconstruction of artifacted or missing electroencephalographic (EEG) data; diagnosis of sleep disorders; modeling of the brain response during memory tasks; and exploration of EEG data from subjects performing a battery of neuropsychological tests. The obtained results demonstrate the capability of the proposed methods to work on problems wi...

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Reference-Based Source Separation Method For Identification of Brain Regions Involved in a Reference State From Intracerebral EEG

Cited by 9 publications

References 17 publications

Integrated Analysis of EEG and fMRI Using Sparsity of Spatial Maps

Integrated Analysis of EEG and fMRI Using Sparsity of Spatial Maps

Interictal EEG noise cancellation: GEVD and DSS based approaches versus ICA and DCCA based methods

New Insights in Prediction and Dynamic Modeling from Non-Gaussian Mixture Processing Methods

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