Effect of Power and Phase Synchronization in Multi-Trial Speech Imagery

Speech imagery is one form of mental imagery which refers to the imagining of speaking a word to oneself silently in the mind without any articulation movement. In this work, electroencephalography (EEG) signals were acquired while speaking and during the imagining of speaking consonant-vowel-consonant (CVC) words in multiple trials of different time frames. Relative powers were computed for each EEG frequency band. It has been observed that relative power of alpha and theta bands was dominant. Phase Locking Value (PLV), a functional brain connectivity parameter has been estimated to understand the phase synchronicity between two brain regions. PLV results show that the left hemispheric frontal and temporal electrodes has maximum phase lock in alpha and theta band during speech and speech imagery process. The combination of brain connectivity estimators and signal processing techniques will thus be a reliable framework for understanding the nature of speech imagery signals captured through EEG.

Download Full-text

Effect of Power and Phase Synchronization in Multi-Trial Speech Imagery

International Journal of Software Science and Computational Intelligence ◽

10.4018/ijssci.2018100104 ◽

2018 ◽

Vol 10 (4) ◽

pp. 44-61 ◽

Cited By ~ 1

Author(s):

Sandhya Chengaiyan ◽

Divya Balathayil ◽

Kavitha Anandan ◽

Christy Bobby Thomas

Keyword(s):

Phase Synchronization ◽

Brain Connectivity ◽

Phase Locking ◽

Brain Regions ◽

Multiple Trials ◽

Phase Locking Value ◽

The Mind ◽

Processing Techniques ◽

Lock In ◽

Time Frames

Download Full-text

Link Prediction Investigation of Dynamic Information Flow in Epilepsy

Journal of Healthcare Engineering ◽

10.1155/2018/8102597 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13

Author(s):

Yan He ◽

Fan Yang ◽

Yunli Yu ◽

Celso Grebogi

Keyword(s):

Link Prediction ◽

Focal Epilepsy ◽

Phase Locking ◽

Brain Regions ◽

Performance Comparison ◽

Brain Disorder ◽

Common Neighbor ◽

Comparison Results ◽

Ictal Activity ◽

Phase Locking Value

As a brain disorder, epilepsy is characterized with abnormal hypersynchronous neural firings. It is known that seizures initiate and propagate in different brain regions. Long-term intracranial multichannel electroencephalography (EEG) reflects broadband ictal activity under seizure occurrence. Network-based techniques are efficient in discovering brain dynamics and offering finger-print features for specific individuals. In this study, we adopt link prediction for proposing a novel workflow aiming to quantify seizure dynamics and uncover pathological mechanisms of epilepsy. A dataset of EEG signals was enrolled that recorded from 8 patients with 3 different types of pharmocoresistant focal epilepsy. Weighted networks are obtained from phase locking value (PLV) in subband EEG oscillations. Common neighbor (CN), resource allocation (RA), Adamic-Adar (AA), and Sorenson algorithms are brought in for link prediction performance comparison. Results demonstrate that RA outperforms its rivals. Similarity, matrix was produced from the RA technique performing on EEG networks later. Nodes are gathered to form sequences by selecting the ones with the highest similarity. It is demonstrated that variations are in accordance with seizure attack in node sequences of gamma band EEG oscillations. What is more, variations in node sequences monitor the total seizure journey including its initiation and termination.

Download Full-text

Classification of Autism Spectrum Disorder from EEG-based Functional Brain Connectivity Analysis

Neural Computation ◽

10.1162/neco_a_01394 ◽

2021 ◽

pp. 1-27

Author(s):

Noura Alotaibi ◽

Koushik Maharatna

Keyword(s):

Machine Learning ◽

Autism Spectrum Disorder ◽

Brain Connectivity ◽

Phase Locking ◽

Children With Autism ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Functional Brain ◽

Graph Theoretic ◽

Phase Locking Value

Abstract Autism is a psychiatric condition that is typically diagnosed with behavioral assessment methods. Recent years have seen a rise in the number of children with autism. Since this could have serious health and socioeconomic consequences, it is imperative to investigate how to develop strategies for an early diagnosis that might pave the way to an adequate intervention. In this study, the phase-based functional brain connectivity derived from electroencephalogram (EEG) in a machine learning framework was used to classify the children with autism and typical children in an experimentally obtained data set of 12 autism spectrum disorder (ASD) and 12 typical children. Specifically, the functional brain connectivity networks have quantitatively been characterized by graph-theoretic parameters computed from three proposed approaches based on a standard phase-locking value, which were used as the features in a machine learning environment. Our study was successfully classified between two groups with approximately 95.8% accuracy, 100% sensitivity, and 92% specificity through the trial-averaged phase-locking value (PLV) approach and cubic support vector machine (SVM). This work has also shown that significant changes in functional brain connectivity in ASD children have been revealed at theta band using the aggregated graph-theoretic features. Therefore, the findings from this study offer insight into the potential use of functional brain connectivity as a tool for classifying ASD children.

Download Full-text

Phase-locking of resting-state brain networks with the gastric basal electrical rhythm

10.1101/2020.10.06.328054 ◽

2020 ◽

Author(s):

Ann S. Choe ◽

Bohao Tang ◽

Kimberly R. Smith ◽

Hamed Honari ◽

Martin A. Lindquist ◽

...

Keyword(s):

Resting State ◽

Phase Locking ◽

Statistical Significance ◽

Null Distribution ◽

Surrogate Data ◽

Brain Regions ◽

Vagal Afferents ◽

Network Disruptions ◽

Time Courses ◽

Phase Locking Value

ABSTRACTA network of myenteric interstitial cells of Cajal in the corpus of the stomach serves as its “pacemaker”, continuously generating a ca. 0.05 Hz electrical slow wave, which is transmitted to the brain chiefly by vagal afferents. A recent study combining resting-state functional MRI (rsfMRI) with concurrent surface electrogastrography (EGG), with cutaneous electrodes placed on the epigastrium, found 12 brain regions with activity that was significantly phase-locked with this gastric basal electrical rhythm. Therefore, we asked whether fluctuations in brain resting state networks (RSNs), estimated using a spatial independent component analysis (ICA) approach, might be synchronized with the stomach. In the present study, in order to determine whether any RSNs are phase-locked with the gastric rhythm, an individual participant underwent 22 scanning sessions; in each, two 15-minute runs of concurrent EGG and rsfMRI data were acquired. EGG data from three sessions had weak gastric signals and were excluded; the other 19 sessions yielded a total of 9.5 hours of data. The rsfMRI data were analyzed using group ICA; RSN time courses were estimated using dual regression; for each run, the phase-locking value (PLV) was computed between each RSN and the gastric signal. To assess statistical significance, PLVs from all pairs of “mismatched” data (EGG and rsfMRI data acquired on different days) were used as surrogate data to generate a null distribution for each RSN. Of a total of 18 RSNs, three were found to be significantly phase-locked with the basal gastric rhythm, namely, a cerebellar network, a dorsal somatosensory-motor network, and a default mode network. Disruptions to the gut-brain axis, which sustains interoceptive feedback between the central nervous system and the viscera, are thought to be involved in various disorders; manifestation of the infra-slow rhythm of the stomach in brain rsfMRI data could be useful for studies in clinical populations.

Download Full-text

Multiple constrained minimum variance beamformer (MCMV) performance in connectivity analyses

10.1101/567768 ◽

2019 ◽

Cited By ~ 1

Author(s):

Adonay S. Nunes ◽

Alexander Moiseev ◽

Nataliia Kozhemiako ◽

Teresa Cheung ◽

Urs Ribary ◽

...

Keyword(s):

Resting State ◽

Brain Connectivity ◽

Brain Activity ◽

Phase Locking ◽

Time Lag ◽

Simulated Data ◽

Brain Regions ◽

Minimum Variance ◽

Linearly Constrained ◽

Signal Cancellation

AbstractFunctional brain connectivity is increasingly being seen as critical for cognition, perception and motor control.Magnetoencephalography and electroencephalography are modalities that offer noninvasive mapping of electrophysiological interactions among brain regions, yet suffer from signal leakage and signal cancellation when estimating brain activity. This leads to biased connectivity values which complicate interpretation. In this study, we test the hypothesis that a Multiple Constrained Minimum Variance beamformer (MCMV) outperforms the more traditional Linearly Constrained Minimum Variance beamformer (LCMV) for estimation of electrophysiological connectivity. To this end, MCMV and LCMV performance is compared in task related analyses with both simulated data and human MEG recordings of visual steady state signals, and in resting state analyses with simulated data and human MEG data of 89 subjects. In task related scenarios connectivity was estimated using coherence and phase locking values, whereas envelope correlations were used for the resting state data. We also introduce a novel Augmented Pairwise MCMV (APW-MCMV) approach for signal leakage suppression in resting state analyses and assess its performance against LCMV and more conventional MCMV approaches. We demonstrate that with MCMV effects of signal mixing and coherent source cancellation are greatly reduced in both task related and resting state conditions, while in contrast to other approaches 0-and short time lag interactions are preserved. In addition, we demonstrate that in resting state analyses, APW-MCMV strongly reduces spurious connections while better controlling for false negatives compared to more conservative measures such as symmetrical orthogonalization.

Download Full-text

Magnetoencephalography-based identification of functional connectivity network disruption following mild traumatic brain injury

Journal of Neurophysiology ◽

10.1152/jn.00513.2016 ◽

2016 ◽

Vol 116 (4) ◽

pp. 1840-1847 ◽

Cited By ~ 16

Author(s):

Ahmad Alhourani ◽

Thomas A. Wozny ◽

Deepa Krishnaswamy ◽

Sudhir Pathak ◽

Shawn A. Walls ◽

...

Keyword(s):

Traumatic Brain Injury ◽

Brain Injury ◽

Functional Connectivity ◽

Mild Traumatic Brain Injury ◽

Phase Locking ◽

Brain Regions ◽

Memory And Attention ◽

Control Subjects ◽

Cortical Regions ◽

Phase Locking Value

Mild traumatic brain injury (mTBI) leads to long-term cognitive sequelae in a significant portion of patients. Disruption of normal neural communication across functional brain networks may explain the deficits in memory and attention observed after mTBI. In this study, we used magnetoencephalography (MEG) to examine functional connectivity during a resting state in a group of mTBI subjects ( n = 9) compared with age-matched control subjects ( n = 15). We adopted a data-driven, exploratory analysis in source space using phase locking value across different frequency bands. We observed a significant reduction in functional connectivity in band-specific networks in mTBI compared with control subjects. These networks spanned multiple cortical regions involved in the default mode network (DMN). The DMN is thought to subserve memory and attention during periods when an individual is not engaged in a specific task, and its disruption may lead to cognitive deficits after mTBI. We further applied graph theoretical analysis on the functional connectivity matrices. Our data suggest reduced local efficiency in different brain regions in mTBI patients. In conclusion, MEG can be a potential tool to investigate and detect network alterations in patients with mTBI. The value of MEG to reveal potential neurophysiological biomarkers for mTBI patients warrants further exploration.

Download Full-text

PHASE SYNCHRONIZATION IN BRAIN NETWORKS DERIVED FROM CORRELATION BETWEEN PROBABILITIES OF RECURRENCES IN FUNCTIONAL MRI DATA

International Journal of Neural Systems ◽

10.1142/s0129065713500032 ◽

2013 ◽

Vol 23 (02) ◽

pp. 1350003 ◽

Cited By ~ 30

Author(s):

D. RANGAPRAKASH ◽

XIAOPING HU ◽

GOPIKRISHNA DESHPANDE

Keyword(s):

Linear Correlation ◽

Resting State ◽

Brain Function ◽

Phase Synchronization ◽

Human Subjects ◽

Brain Connectivity ◽

Brain Networks ◽

Resting State Fmri ◽

Brain Regions ◽

Fmri Data

It is increasingly being recognized that resting state brain connectivity derived from functional magnetic resonance imaging (fMRI) data is an important marker of brain function both in healthy and clinical populations. Though linear correlation has been extensively used to characterize brain connectivity, it is limited to detecting first order dependencies. In this study, we propose a framework where in phase synchronization (PS) between brain regions is characterized using a new metric "correlation between probabilities of recurrence" (CPR) and subsequent graph-theoretic analysis of the ensuing networks. We applied this method to resting state fMRI data obtained from human subjects with and without administration of propofol anesthetic. Our results showed decreased PS during anesthesia and a biologically more plausible community structure using CPR rather than linear correlation. We conclude that CPR provides an attractive nonparametric method for modeling interactions in brain networks as compared to standard correlation for obtaining physiologically meaningful insights about brain function.

Download Full-text

Fast Feature Selection to Compare Broadband with Narrowband Phase Synchronization in Brain-computer Interfaces

Methods of Information in Medicine ◽

10.1055/s-0038-1625399 ◽

2007 ◽

Vol 46 (02) ◽

pp. 160-163 ◽

Cited By ~ 4

Author(s):

E. Gysels ◽

P. Celka ◽

P. Renevey

Keyword(s):

Feature Selection ◽

Phase Synchronization ◽

Brain Activity ◽

Phase Locking ◽

Hand Movement ◽

Eeg Signals ◽

Communication Devices ◽

The One ◽

Phase Locking Value ◽

Mental Tasks

Summary Objective : Brain-computer interface (BCI) research aims at developing communication devices for the motor disabled. Such devices are not driven by muscle activity, but by brain activity recorded during different mental tasks. We present here the comparison of phase synchronization and power spectral density (PSD) features, computed from broadband and narrowband filtered EEG signals and their ability to discriminate three mental tasks. Methods : EEG signals were recorded from five subjects while performing left and right hand movement imagination and word generation. We applied a modified Fast Correlation Based Filter (FCBF) [9] for the purpose of feature selection. Results : We found that the features were selected from electrode signals corresponding to neurophysiological evidence, i.e. electrodes lying over the motor cortex.PSD and phase locking value (PLV) features were more discriminative when computed from narrowband (8-12 Hz) and broadband (8-30 Hz) filtered signals respectively. Conclusions : The generalization performance is as good as the one obtained with SVM-rfe, but this algorithm is faster and selects fewer features. These properties may make FCBF a valuable tool for further improvement of BCIs.

Download Full-text

Phase-locking of resting-state brain networks with the gastric basal electrical rhythm

PLoS ONE ◽

10.1371/journal.pone.0244756 ◽

2021 ◽

Vol 16 (1) ◽

pp. e0244756

Author(s):

Ann S. Choe ◽

Bohao Tang ◽

Kimberly R. Smith ◽

Hamed Honari ◽

Martin A. Lindquist ◽

...

Keyword(s):

Resting State ◽

Phase Locking ◽

Statistical Significance ◽

Null Distribution ◽

Surrogate Data ◽

Brain Regions ◽

Vagal Afferents ◽

Network Disruptions ◽

Time Courses ◽

Phase Locking Value

A network of myenteric interstitial cells of Cajal in the corpus of the stomach serves as its “pacemaker”, continuously generating a ca 0.05 Hz electrical slow wave, which is transmitted to the brain chiefly by vagal afferents. A recent study combining resting-state functional MRI (rsfMRI) with concurrent surface electrogastrography (EGG), with cutaneous electrodes placed on the epigastrium, found 12 brain regions with activity that was significantly phase-locked with this gastric basal electrical rhythm. Therefore, we asked whether fluctuations in brain resting state networks (RSNs), estimated using a spatial independent component analysis (ICA) approach, might be synchronized with the stomach. In the present study, in order to determine whether any RSNs are phase-locked with the gastric rhythm, an individual participant underwent 22 scanning sessions; in each, two 15-minute runs of concurrent EGG and rsfMRI data were acquired. EGG data from three sessions had weak gastric signals and were excluded; the other 19 sessions yielded a total of 9.5 hours of data. The rsfMRI data were analyzed using group ICA; RSN time courses were estimated; for each run, the phase-locking value (PLV) was computed between each RSN and the gastric signal. To assess statistical significance, PLVs from all pairs of “mismatched” data (EGG and rsfMRI data acquired on different days) were used as surrogate data to generate a null distribution for each RSN. Of a total of 18 RSNs, three were found to be significantly phase-locked with the basal gastric rhythm, namely, a cerebellar network, a dorsal somatosensory-motor network, and a default mode network. Disruptions to the gut-brain axis, which sustains interoceptive feedback between the central nervous system and the viscera, are thought to be involved in various disorders; manifestation of the infra-slow rhythm of the stomach in brain rsfMRI data could be useful for studies in clinical populations.

Download Full-text