A Tensor Decomposition-Based Approach for Detecting Dynamic Network States From EEG

Mahyari, Arash Golibagh; Zoltowski, David M.; Bernat, Edward M.; Aviyente, Selin

doi:10.1109/tbme.2016.2553960

Cited by 49 publications

(32 citation statements)

References 69 publications

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“…Thus, u k 1 is the spatial component that captures most of the energy of the state pattern k, and u k 2 similarly captures the second largest part of energy of that sate pattern (Mahyari, Zoltowski, Bernat, & Aviyente, 2017). Thus, u k 1 is the spatial component that captures most of the energy of the state pattern k, and u k 2 similarly captures the second largest part of energy of that sate pattern (Mahyari, Zoltowski, Bernat, & Aviyente, 2017).…”

Section: Singular Value Decompositionmentioning

confidence: 99%

“…nal with positive real entries, σ k l is the singular values of X k . Thus, u k 1 is the spatial component that captures most of the energy of the state pattern k, and u k 2 similarly captures the second largest part of energy of that sate pattern (Mahyari, Zoltowski, Bernat, & Aviyente, 2017).…”

Section: Singular Value Decompositionmentioning

confidence: 99%

“…The first common spatial component captures the most part of the power (around more than 70% in this study) across the collection of the dynamic functional connectivity matrix and would correspond to the stationary connectivity as estimated in static connectivity(Alter, Brown, & Botstein, 2000;Mahyari et al, 2017). The first common spatial component captures the most part of the power (around more than 70% in this study) across the collection of the dynamic functional connectivity matrix and would correspond to the stationary connectivity as estimated in static connectivity(Alter, Brown, & Botstein, 2000;Mahyari et al, 2017).…”

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confidence: 99%

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Frequency‐specific age‐related decreased brain network diversity in cognitively healthy elderly: A whole‐brain data‐driven analysis

Lou

Wang

Wong

et al. 2018

Human Brain Mapping

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Age‐related changes in functional brain network have been well documented. However, recent studies have suggested the nonstationary properties of the functional connectivity of the brain, and little is known about the changes of functional connectivity dynamics during aging. In this study, a two‐step singular value decomposition was introduced to capture the dynamic patterns of the time‐varying functional connectivity in different frequency intervals, and the whole‐brain and regional brain diversity were quantified by using Shannon entropy. The relationships between age and functional connectivity dynamics were investigated in a relatively large sample cohort of cognitively healthy elderly (N = 188, ages 65–80). The results showed an age‐related decreased diversity in the whole brain as well as in the right inferior frontal gyrus, right amygdala, right hippocampus, left parahippocampal, and left inferior parietal gyrus in the frequency interval of 0.06–0.12 Hz. In addition, the whole‐brain diversity during resting state could also reflect the general mental flexibility. This study provided the first evidence of frequency‐specific age effects on the functional connectivity dynamics in cognitively healthy elderly, and may shed new light on the dynamic functional connectivity analysis of aging and neurodegenerative diseases.

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Section: Singular Value Decompositionmentioning

confidence: 99%

Section: Singular Value Decompositionmentioning

confidence: 99%

mentioning

confidence: 99%

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Frequency‐specific age‐related decreased brain network diversity in cognitively healthy elderly: A whole‐brain data‐driven analysis

Lou

Wang

Wong

et al. 2018

Human Brain Mapping

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“…There are some novelty aspects in this work, that need to be put in the context of contemporary practice in neuroscience research and streaming data analysis. First, we need to underline our choice to work with dynamic phase synchrony patterns, casting new empirical evidence about the benefits of chronnectomics (“chronos” = time + “connectomics”), an emerging branch of network neuroscience that focuses on the dynamics of brain-network (self)organization phenomena [ 58 – 61 ]. Phase locking computations can be implemented efficiently from multisite recordings, as already has been pointed out by a recent work [ 62 ] and indicated in the Appendix .…”

Section: Discussionmentioning

confidence: 99%

Exploiting the heightened phase synchrony in patients with neuromuscular disease for the establishment of efficient motor imagery BCIs

Georgiadis

Laskaris²,

Nikolopoulos

et al. 2018

J NeuroEngineering Rehabil

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BackgroundPhase synchrony has extensively been studied for understanding neural coordination in health and disease. There are a few studies concerning the implications in the context of BCIs, but its potential for establishing a communication channel in patients suffering from neuromuscular disorders remains totally unexplored. We investigate, here, this possibility by estimating the time-resolved phase connectivity patterns induced during a motor imagery (MI) task and adopting a supervised learning scheme to recover the subject’s intention from the streaming data.MethodsElectroencephalographic activity from six patients suffering from neuromuscular disease (NMD) and six healthy individuals was recorded during two randomly alternating, externally cued, MI tasks (clenching either left or right fist) and a rest condition. The metric of Phase locking value (PLV) was used to describe the functional coupling between all recording sites. The functional connectivity patterns and the associate network organization was first compared between the two cohorts. Next, working at the level of individual patients, we trained support vector machines (SVMs) to discriminate between “left” and “right” based on different instantiations of connectivity patterns (depending on the encountered brain rhythm and the temporal interval). Finally, we designed and realized a novel brain decoding scheme that could interpret the intention from streaming connectivity patterns, based on an ensemble of SVMs.ResultsThe group-level analysis revealed increased phase synchrony and richer network organization in patients. This trend was also seen in the performance of the employed classifiers. Time-resolved connectivity led to superior performance, with distinct SVMs acting as local experts, specialized in the patterning emerged within specific temporal windows (defined with respect to the external trigger). This empirical finding was further exploited in implementing a decoding scheme that can be activated without the need of the precise timing of a trigger.ConclusionThe increased phase synchrony in NMD patients can turn to a valuable tool for MI decoding. Considering the fast implementation for the PLV pattern computation in multichannel signals, we can envision the development of efficient personalized BCI systems in assistance of these patients.Electronic supplementary materialThe online version of this article (10.1186/s12984-018-0431-6) contains supplementary material, which is available to authorized users.

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“…Note that HOSVD was already used to extract a representative Epoch from a 4-modes FC dataset in [40] and in [41], using a Higher-Order robust principal component analysis. However, both methods suffer from a lack of sparsity in their resulting graphs, entailing results that are difficult to analyse as they do not single out any critical functional connectivity that would characterise the seizure onset.…”

Section: Related Work On Dimentionality Reductionmentioning

confidence: 99%

Multiplex Network Inference With Sparse Tensor Decomposition for Functional Connectivity

Frusque

Jung

Borgnat

et al. 2020

IEEE Trans. on Signal and Inf. Process. over Networks

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Functional connectivity (FC) is a graphlike data structure commonly used by neuroscientists to study the dynamic behaviour of brain activity. However, these analyses rapidly become complex and timeconsuming, since the number of connectivity components to be studied is quadratic with the number of electrodes. In this work, we address the problem of clustering FC into relevant ensembles of simultaneously activated components, yielding a multiplex network that reveals characteristic patterns of the epileptic seizures of a given patient. While k−means is certainly the most popular method for data clustering, it is known to perform poorly on large dimensional data sets, and to be highly sensitive to noise. To overcome the socalled curse of dimensionality, we propose a new tensor decomposition to reduce the size of the data set formed by FC time-series recorded for several seizures, prior to apply k-means. We propose an adapted procedure to infer a multiplex network from several FC time series, and we emphasise one particular variant that imposes sparsity constraint. Then, we conduct a real case study, applying the proposed sparse tensor decomposition to iEEG data to infer a multiplex network corresponding to the different stages of an epileptic seizure.

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A Tensor Decomposition-Based Approach for Detecting Dynamic Network States From EEG

Abstract: The proposed tensor-based method captures the topological structure of FCNs which provides more accurate change-point-detection and state summarization.

Cited by 49 publications

References 69 publications

Frequency‐specific age‐related decreased brain network diversity in cognitively healthy elderly: A whole‐brain data‐driven analysis

Frequency‐specific age‐related decreased brain network diversity in cognitively healthy elderly: A whole‐brain data‐driven analysis

Exploiting the heightened phase synchrony in patients with neuromuscular disease for the establishment of efficient motor imagery BCIs

Multiplex Network Inference With Sparse Tensor Decomposition for Functional Connectivity

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