Motif-Synchronization: A new method for analysis of dynamic brain networks with EEG

Rosário, Raphael Silva do; Cardoso, P.T.; Muñoz, Miguel A.; Montoya, Pedro; Miranda, José García Vivas

doi:10.1016/j.physa.2015.07.018

Cited by 47 publications

(43 citation statements)

References 28 publications

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“…Graphs connectivity matrices were calculated using the motifs’ synchronization method ( Rosario et al, 2015 ). The general principle is to divide the original time-series into smaller ensembles of data points (here we used three data points, as suggested by ( Rosario et al, 2015 )) and to label these new patterns according to Fig. 1A .…”

Section: Methodsmentioning

confidence: 99%

EEG sensorimotor rhythms’ variation and functional connectivity measures during motor imagery: linear relations and classification approaches

Filho

Attux

Castellano

2017

PeerJ

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Hands motor imagery (MI) has been reported to alter synchronization patterns amongst neurons, yielding variations in the mu and beta bands’ power spectral density (PSD) of the electroencephalography (EEG) signal. These alterations have been used in the field of brain-computer interfaces (BCI), in an attempt to assign distinct MI tasks to commands of such a system. Recent studies have highlighted that information may be missing if knowledge about brain functional connectivity is not considered. In this work, we modeled the brain as a graph in which each EEG electrode represents a node. Our goal was to understand if there exists any linear correlation between variations in the synchronization patterns—that is, variations in the PSD of mu and beta bands—induced by MI and alterations in the corresponding functional networks. Moreover, we (1) explored the feasibility of using functional connectivity parameters as features for a classifier in the context of an MI-BCI; (2) investigated three different types of feature selection (FS) techniques; and (3) compared our approach to a more traditional method using the signal PSD as classifier inputs. Ten healthy subjects participated in this study. We observed significant correlations (p < 0.05) with values ranging from 0.4 to 0.9 between PSD variations and functional network alterations for some electrodes, prominently in the beta band. The PSD method performed better for data classification, with mean accuracies of (90 ± 8)% and (87 ± 7)% for the mu and beta band, respectively, versus (83 ± 8)% and (83 ± 7)% for the same bands for the graph method. Moreover, the number of features for the graph method was considerably larger. However, results for both methods were relatively close, and even overlapped when the uncertainties of the accuracy rates were considered. Further investigation regarding a careful exploration of other graph metrics may provide better alternatives.

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Section: Methodsmentioning

confidence: 99%

EEG sensorimotor rhythms’ variation and functional connectivity measures during motor imagery: linear relations and classification approaches

Filho

Attux

Castellano

2017

PeerJ

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“…The features proposed herein are detailed in the subsections below and only consider motifs of degree n =3 and lag value λ =1. These parameters have been suggested in the past for related tasks [39, 50].…”

Section: Methodsmentioning

confidence: 99%

“…Functional connectivity gives insight into the dynamic neural interaction of the different regions of the brain. Recently, motif synchronization has been proposed as a functional connectivity analysis tool [50] and measures the simultaneous appearance of motifs in two time series. For two motif series X m and Y m , c ( X m ; Y m ) is defined as the highest number of times in which the same motif can appear in Y m shortly after it appeared in X m for different delay times, i.e.,cXm;Ym=cXY=maxtrue∑i=1lmJiτ0,true∑i=1lmJiτ1,…,true∑i=1lmJiτn,withJiτi=1,if XMi=YMi+τ ,…”

Section: Methodsmentioning

confidence: 99%

Fusion of Motif- and Spectrum-Related Features for Improved EEG-Based Emotion Recognition

Tiwari

Falk

2019

Computational Intelligence and Neuroscience

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Emotion recognition is a burgeoning field allowing for more natural human-machine interactions and interfaces. Electroencephalography (EEG) has shown to be a useful modality with which user emotional states can be measured and monitored, particularly primitives such as valence and arousal. In this paper, we propose the use of ordinal pattern analysis, also called motifs, for improved EEG-based emotion recognition. Motifs capture recurring structures in time series and are inherently robust to noise, thus are well suited for the task at hand. Several connectivity, asymmetry, and graph-theoretic features are proposed and extracted from the motifs to be used for affective state recognition. Experiments with a widely used public database are conducted, and results show the proposed features outperforming benchmark spectrum-based features, as well as other more recent nonmotif-based graph-theoretic features and amplitude modulation-based connectivity/asymmetry measures. Feature and score-level fusion suggest complementarity between the proposed and benchmark spectrum-based measures. When combined, the fused models can provide up to 9% improvement relative to benchmark features alone and up to 16% to nonmotif-based graph-theoretic features.

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“…al. [17] used the ordinal patterns observed in EEG datasets, also known as "motifs" [18], to construct time varying networks and analysed their evolution along time and the properties of the averaged functional network. Specifically, the amount of synchronization between a pair of recorded electrodes of an EEG was obtained by evaluating the number of ordinal patterns co-ocurring at the same time but also at a given lag λ = 1 time steps.…”

mentioning

confidence: 99%

Ordinal synchronization: Using ordinal patterns to capture interdependencies between time series

Echegoyen

Vera-Ávila

Sevilla-Escoboza

et al. 2019

Chaos, Solitons & Fractals

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We introduce Ordinal Synchronization (OS) as a new measure to quantify synchronization between dynamical systems. OS is calculated from the extraction of the ordinal patterns related to two time series, their transformation into D-dimensional ordinal vectors and the adequate quantification of their alignment. OS provides a fast and robust-to noise tool to assess synchronization without any implicit assumption about the distribution of data sets nor their dynamical properties, capturing in-phase and anti-phase synchronization. Furthermore, varying the length of the ordinal vectors required to compute OS it is possible to detect synchronization at different time scales. We test the performance of OS with data sets coming from unidirectionally coupled electronic Lorenz oscillators and brain imaging datasets obtained from magnetoencephalographic recordings, comparing the performance of OS with other classical metrics that quantify synchronization between dynamical systems.

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Motif-Synchronization: A new method for analysis of dynamic brain networks with EEG

Cited by 47 publications

References 28 publications

EEG sensorimotor rhythms’ variation and functional connectivity measures during motor imagery: linear relations and classification approaches

EEG sensorimotor rhythms’ variation and functional connectivity measures during motor imagery: linear relations and classification approaches

Fusion of Motif- and Spectrum-Related Features for Improved EEG-Based Emotion Recognition

Ordinal synchronization: Using ordinal patterns to capture interdependencies between time series

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