Cross Validation for Selection of Cortical Interaction Models From Scalp EEG or MEG

Cheung, Bing Leung Patrick; Nowak, Robert; Lee, Hyong Chol; Drongelen, Wim van; Veen, B.D. Van

doi:10.1109/tbme.2011.2174991

Cited by 14 publications

(12 citation statements)

References 51 publications

(53 reference statements)

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“…Noise in the EEG data results in correlation between the noise components of the estimated source signals that introduces bias into the estimated connectivity values. The SNR requirements are greatly reduced using a novel approach we recently introduced and validated (Cheung et al, 2010, 2012). In brief, this approach jointly solves the equations modeling connectivity in source space and the physics of measuring the source activity with EEG by means of a state-space formulation and maximum-likelihood estimation techniques.…”

Section: Introductionmentioning

confidence: 99%

Disrupted directed connectivity along the cingulate cortex determines vigilance after sleep deprivation

et al. 2013

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The cingulate cortex is regarded as the backbone of structural and functional connectivity of the brain. While its functional connectivity has been intensively studied, little is known about its effective connectivity, its modulation by behavioral states, and its involvement in cognitive performance. Given their previously reported effects on cingulate functional connectivity, we investigated how eye-closure and sleep deprivation changed cingulate effective connectivity, estimated from resting-state high-density electroencephalography (EEG) using a novel method to calculate Granger Causality directly in source space. Effective connectivity along the cingulate cortex was dominant in the forward direction. Eyes-open connectivity in the forward direction was greater compared to eyes-closed, in well-rested participants. The difference between eyes-open and eyes-closed connectivity was attenuated and no longer significant after sleep deprivation. Individual variability in the forward connectivity after sleep deprivation predicted subsequent task performance, such that those subjects who showed a greater increase in forward connectivity between the eyes-open and the eyes-closed periods also performed better on a sustained attention task. Effective connectivity in the opposite, backward, direction was not affected by whether the eyes were open or closed or by sleep deprivation. These findings indicate that the effective connectivity from posterior to anterior cingulate regions is enhanced when a well-rested subject has his eyes open compared to when they are closed. Sleep deprivation impairs this directed information flow, proportional to its deleterious effect on vigilance. Therefore, sleep may play a role in the maintenance of waking effective connectivity.

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Section: Introductionmentioning

confidence: 99%

Disrupted directed connectivity along the cingulate cortex determines vigilance after sleep deprivation

et al. 2013

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“…where A(k) and B(k) are the model co-efficient matrices, p is the optimal model order chosen by Schwarz Bayesian Criterion (SBC) [18], [19], n is the time index and k is the lag. MVAR model parameter are estimated by the Kalman filter [20].…”

Section: Wavelet Coherence (Wc)mentioning

confidence: 99%

Topographical pattern analysis using wavelet based coherence connectivity estimation in the distinction of meditation and non-meditation EEG

Shaw

Routray

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Classification of EEG signal involved in a particular cognitive activity has found many application in brain-computer interface (BCI). In specific, use of classification algorithms to highly multivariate non-stationary recordings like EEG is a challenging and promising task. This study investigated two substantial novelty of the topics, (1) Distinction between meditation (Kriya Yoga) and non-meditation state allied EEG, (2) Characterization of the underlying mechanism of cognitive process that is associated with meditation using topographical analysis. The topographic wavelet coherence based brain connectivity between two different groups is shown. Two groups of data, one with 23 meditators (meditator group) and other with ten non-meditators (controlled group) are analyzed. The spatial distribution between two groups can be well distinguished by the topographical approach. The quantification has been done by the colour intensity embedded in the topographical plots. The wavelet coherence is found to be a different parameter to represent the distinctiveness between two groups. The time-frequency quantification regarding wavelet coherence spectrum is shown the unique patterns among meditators and non-meditators. Thus time-frequency based wavelet coherence has found to be an unusual brain pattern in the distinction between meditators and non-meditators.

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“…The real valued parameter reflects the linear relationship between channel and channel at the delay . In the stationary case, the optimum order p of an MVAR model can be estimated using different methods such as Akaike Information Criterion (AIC) and Schwarz's Bayesian Criterion (SBC) [101,124].SBC has been shown to be preferable over AIC for time series analysis [125]. For a reliable estimation of the MVAR parameters, the number of data points available ( ) need to be significantly larger than the number of parameters ( ) or equivalently, the signal length ( ) should be much longer than [90].…”

Section: Multivariate Autoregressive Modelmentioning

confidence: 99%

“…Due to its non-invasive nature, high temporal resolution and low cost, scalp EEG is often used as the basis for studying brain connectivity [95][96][97][98][99][100][101]. ].…”

Section: Introductionmentioning

confidence: 99%

“…both propagations 1→2→3 and propagation 1→3 are reflected in it), dDTF [112] can separate direct flows from indirect flows [9,10]. The two frequency domain approaches to connectivity analysis (PDC vs. DTF) are designed to assess different properties in the signal with each having its own advantages and disadvantages [22,101,106,109,113]. The measure gPDC [111] combines the idea of DTF (to show the influencing effects) and PDC (to reflect influenced effects) between channel i and channel j.…”

Section: Introductionmentioning

confidence: 99%

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Newborn EEG connectivity analysis using time-frequency signal processing techniques

Omidvarnia¹

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Scalp electroencephalography (EEG) monitoring in early life is providing vital assistance to clinical management during diagnosis and treatment of developing brain disorders. It is also a useful tool for predicting long-term neurological outcomes of perinatal injuries. Continuous evolution of cerebral structures during early brain maturation imposes salient changes in the temporal morphology of newborn EEG patterns. Therefore, dealing with neonatal EEG signals is technically very challenging. The current dominant approach to newborn EEG assessment is based on visual inspection of EEG recordings by an expert. This process is highly subjective and also, very timeconsuming. More importantly, it requires a high level of expertise for appropriate interpretation. On the other hand, neonatal intensive care units (NICUs) in most countries do not have ready access to high-level EEG expertise. The development of automatic newborn EEG analysis systems is therefore vital to improve international newborn health.This dissertation is focused on developing scalp EEG connectivity analysis methods for objective monitoring of newborn EEG signals. Three important characteristics of the signals are taken into consideration for the proposed methodologies: time-varying behavior, directional relationships between channels and frequency-specific fluctuations of amplitudes. It leads to a more comprehensive insight into the scalp-level electrical interactions between different cortical areas of the newborn brain in time and/or frequency domains. Two types of newborn brain abnormalities including seizures and intra-ventricular hemorrhage (IVH) and their impact on scalp EEG connectivity are investigated. Also, neonatal electric resting state networks (eRSNs) are characterized using EEG recordings of healthy fullterms, healthy preterms and preemies with IVH as well as a set of newborn functional magnetic resonance imaging (fMRI) datasets.The first contribution of this research is a new time-frequency based approach for estimating the generalized phase synchrony (GePS) among multichannel newborn EEG signals using the linear relationships between their instantaneous frequency laws. Since the underlying signals are usually multicomponent, a decomposition method like multi-channel empirical mode decomposition is used to simultaneously decompose the multi-channel signals into their intrinsic mode functions (IMFs). The results confirm that the GePS value within EEG channels increases significantly during ictal periods. A statistically consistent phase coupling is also observed within the non-seizure segments supporting the concept of constant inter-hemispheric connectivity in the newborn brain during interictal periods.The second contribution is a time-frequency method for measuring directional interactions over time and frequency from scalp-recorded newborn EEG signals in a way that is less affected by volume conduction and amplitude scaling. The time-varying generalized partial directed coherence method is modified, by orthogonalization ...

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Cross Validation for Selection of Cortical Interaction Models From Scalp EEG or MEG

Cited by 14 publications

References 51 publications

Disrupted directed connectivity along the cingulate cortex determines vigilance after sleep deprivation

Disrupted directed connectivity along the cingulate cortex determines vigilance after sleep deprivation

Topographical pattern analysis using wavelet based coherence connectivity estimation in the distinction of meditation and non-meditation EEG

Newborn EEG connectivity analysis using time-frequency signal processing techniques

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