title: Integrated Analysis of EEG and fMRI Using Sparsity of Spatial Maps.
abstract: 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.
authors.affiliation: CIPCE, Electrical and Computer Engineering Department, University of Tehran, Tehran, Iran.|GIPSA-LAB, University of Grenoble Alpes, Domaine universitaire- BP 46, 38402, Grenoble Cedex, France.|Department of Electrical Engineering, Hashtgerd Branch, Islamic Azad University, Hashtgerd, Iran.
authors.forename: S
authors.initials: S
authors.lastname: Samadi
authors.affiliation: CIPCE, Electrical and Computer Engineering Department, University of Tehran, Tehran, Iran. hszadeh@ut.ac.ir.|Radiology Image Analysis Laboratory, Henry Ford Health System, Detroit, MI, 48202, USA. hszadeh@ut.ac.ir.|School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran. hszadeh@ut.ac.ir.
authors.forename: H
authors.identifier: 0000-0002-7302-6856
authors.initials: H
authors.lastname: Soltanian-Zadeh
authors.affiliation: GIPSA-LAB, University of Grenoble Alpes, Domaine universitaire- BP 46, 38402, Grenoble Cedex, France.|Institut Universitaire de France, Paris, France.
authors.forename: C
authors.initials: C
authors.lastname: Jutten
mesh_terms: D001499:Bayes Theorem; D001921:Brain / Q000000981:diagnostic imaging* / Q000502:physiology; D001931:Brain Mapping / Q000379:methods*; D004569:Electroencephalography; D001330:Electronic Data Processing; D005260:Female; D059907:Functional Neuroimaging; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D009010:Monte Carlo Method
keywords: Elastic net; Integration analysis; Pareto optimization; Referenced-based signal processing; Weighted sparse decomposition