161. Penalized parafac analysis of resting-state EEG

Montes, Eduardo Martínez; Sánchez-Bornot, José M.; Sosa, P.A. Valdés

doi:10.1016/j.clinph.2008.04.177

Cited by 9 publications

(14 citation statements)

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“…We can iterate this procedure to estimate matrix A until it converges. We can have the following lemma with a similar proof shown in the paper [38]. Lemma 1.…”

Section: Proposed Non-gaussian Penalized Parafacmentioning

confidence: 65%

“…In some applications, imposing the related meaningful constraint can improve the accuracy and interpretation of the solutions. Without loss of generality [38], if we want to add one penalization P(A) on the mode A, we can modify the first equation from formula (11) as:…”

Section: Parafacmentioning

confidence: 99%

“…However, in the application of fMRI, one of the main tasks is to locate the active regions of the spatial components. We may only need to impose the constraints on each spatial component of the spatial mode [38]. In papers Martínez-Montes et al [39,40], the authors proposed a new penalty based on the information entropy for the spatial mode of the constrained PARAFAC analysis of resting EEG that allowed the identification in time, frequency and space of those brain networks with minimum spectral entropy.…”

Section: Introductionmentioning

confidence: 99%

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Non-Gaussian Penalized PARAFAC Analysis for fMRI Data

Liang

Zou

Hong

2019

Front. Appl. Math. Stat.

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Independent Component Analysis (ICA) method has been used widely and successfully in functional magnetic resonance imaging (fMRI) data analysis for both single and group subjects. As an extension of the ICA, tensorial probabilistic ICA (TPICA) is used to decompose fMRI group data into three modes: subject, temporal and spatial. However, due to the independent constraint of the spatial components, TPICA is not very efficient in the presence of overlapping of active regions of different spatial components. Parallel factor analysis (PARAFAC) is another method used to process three-mode data and can be solved by alternating least-squares. PARAFAC may converge into some degenerate solutions if the matrix of one mode is collinear. It is reasonable to find significant collinear relationships within subject mode of two similar subjects in group fMRI data. Thus, both TPICA and PARAFAC have unavoidable drawbacks. In this paper, we try to alleviate both overlapping and collinearity issues by integrating the characters of PARAFAC and TPICA together by imposing a non-Gaussian penalty term to each spatial component under the PARAFAC framework. This proposed algorithm can then regulate the spatial components, as the high non-Gaussianity can possible avoid the degenerate solutions aroused by collinearity issue, and eliminate the independent constraint of the spatial components to bypass the overlapping issue. The algorithm outperforms TPICA and PARAFAC on the simulation data. The results of this algorithm on real fMRI data are also consistent with other algorithms.

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“…We can iterate this procedure to estimate matrix A until it converges. We can have the following lemma with a similar proof shown in the paper [38]. Lemma 1.…”

Section: Proposed Non-gaussian Penalized Parafacmentioning

confidence: 65%

Section: Parafacmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Non-Gaussian Penalized PARAFAC Analysis for fMRI Data

Liang

Zou

Hong

2019

Front. Appl. Math. Stat.

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“…A core-consistency value of 100 means that the data conforms exactly to the model, while values lower than 85 could be an indication of too many components. 16 , 18 Two-, three-, and four-factor models were applied to the individual information of every participant for the different conditions. Absolute values were taken to determine the magnitude of the responses.…”

Section: Methodsmentioning

confidence: 99%

Cortical mapping of painful electrical stimulation by quantitative electroencephalography: unraveling the time–frequency–channel domain

Goudman

Laton

Brouns

et al. 2017

JPR

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The goal of this study was to capture the electroencephalographic signature of experimentally induced pain and pain-modulating mechanisms after painful peripheral electrical stimulation to determine one or a selected group of electrodes at a specific time point with a specific frequency range. In the first experiment, ten healthy participants were exposed to stimulation of the right median nerve while registering brain activity using 32-channel electroencephalography. Electrical stimulations were organized in four blocks of 20 stimuli with four intensities – 100%, 120%, 140%, and 160% – of the electrical pain threshold. In the second experiment, 15 healthy participants received electrical stimulation on the dominant median nerve before and during the application of a second painful stimulus. Raw data were converted into the time–frequency domain by applying a continuous wavelet transform. Separated domain information was extracted by calculating Parafac models. The results demonstrated that it is possible to capture a reproducible cortical neural response after painful electrical stimulation, more specifically at 250 milliseconds poststimulus, at the midline electrodes Cz and FCz with predominant δ-oscillations. The signature of the top-down nociceptive inhibitory mechanisms is δ-activity at 235 ms poststimulus at the prefrontal electrodes. This study presents a methodology to overcome the a priori determination of the regions of interest to analyze the brain response after painful electrical stimulation.

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“…The ONN condition is equivalent to specifying spatially nonoverlapping EEG sources. This requirement, in addition to the smooth Lasso type constraints [44], [46], [49], results in the identification of sparse isolated clustered components that were used to identify distinct cognitive processes involved in face processing.…”

Section: A Matrix Eeg Inverse Problemmentioning

confidence: 99%

Tensor Analysis and Fusion of Multimodal Brain Images

et al. 2015

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Current high-throughput data acquisition technologies probe dynamical systems with different imaging modalities, generating massive data sets at different spatial and temporal resolutions posing challenging problems in multimodal data fusion. A case in point is the attempt to parse out the brain structures and networks that underpin human cognitive processes by analysis of different neuroimaging modalities (functional MRI, EEG, NIRS etc.). We emphasize that the multimodal, multi-scale nature of neuroimaging data is well reflected by a multi-way (tensor) structure where the underlying processes can be summarized by a relatively small number of components or "atoms". We introduce Markov-Penrose diagrams - an integration of Bayesian DAG and tensor network notation in order to analyze these models. These diagrams not only clarify matrix and tensor EEG and fMRI time/frequency analysis and inverse problems, but also help understand multimodal fusion via Multiway Partial Least Squares and Coupled Matrix-Tensor Factorization. We show here, for the first time, that Granger causal analysis of brain networks is a tensor regression problem, thus allowing the atomic decomposition of brain networks. Analysis of EEG and fMRI recordings shows the potential of the methods and suggests their use in other scientific domains.Comment: 23 pages, 15 figures, submitted to Proceedings of the IEE

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161. Penalized parafac analysis of resting-state EEG

Cited by 9 publications

References 0 publications

Non-Gaussian Penalized PARAFAC Analysis for fMRI Data

Non-Gaussian Penalized PARAFAC Analysis for fMRI Data

Cortical mapping of painful electrical stimulation by quantitative electroencephalography: unraveling the time–frequency–channel domain

Tensor Analysis and Fusion of Multimodal Brain Images

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161. Penalized parafac analysis of resting-state EEG

Cited by 9 publications

References 0 publications

Non-Gaussian Penalized PARAFAC Analysis for fMRI Data

Non-Gaussian Penalized PARAFAC Analysis for fMRI Data

Cortical mapping of painful electrical stimulation by quantitative electroencephalography: unraveling the time&ndash;frequency&ndash;channel domain

Tensor Analysis and Fusion of Multimodal Brain Images

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Cortical mapping of painful electrical stimulation by quantitative electroencephalography: unraveling the time–frequency–channel domain