State-Space Multivariate Autoregressive Models for Estimation of Cortical Connectivity from EEG

A state-space formulation is introduced for estimating multivariate autoregressive (MVAR) models of cortical connectivity from noisy, scalp recorded EEG. A state equation represents the MVAR model of cortical dynamics while an observation equation describes the physics relating the cortical signals to the measured EEG and the presence of spatially correlated noise. We assume the cortical signals originate from known regions of cortex, but that the spatial distribution of activity within each region is unknown. An expectation maximization algorithm is developed to directly estimate the MVAR model parameters, the spatial activity distribution components, and the spatial covariance matrix of the noise from the measured EEG. Simulation and analysis demonstrate that this integrated approach is less sensitive to noise than two-stage approaches in which the cortical signals are first estimated from EEG measurements, and next an MVAR model is fit to the estimated cortical signals. The method is further demonstrated by estimating conditional Granger causality using EEG data collected while subjects passively watch a movie.

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“…E { a } denotes the expectation of the random variable a . A preliminary version of this work appeared in [22]. …”

Section: Introductionmentioning

confidence: 99%

Estimation of Cortical Connectivity From EEG Using State-Space Models

Cheung

Riedner

Tononi

et al. 2010

IEEE Trans. Biomed. Eng.

102

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“…Inspired by Cheung et al (2009), the optimal model order of the time-varying MVAR model was calculated by ARFIT algorithm (Omidvarnia et al, 2011). Both the time-invariant parameters of the MVAR model and its optimum order p were estimated by ARFIT package.…”

Section: Functional Brain Networkmentioning

confidence: 99%

“…Here, x is N-channel signal, w is a vector white noise, and the matrices A r are given by Cheung et al (2009) and Omidvarnia et al ( 2011):…”

Section: Functional Brain Networkmentioning

confidence: 99%

Study of Human Tacit Knowledge Based on Electroencephalogram Signal Characteristics

et al. 2021

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Tacit knowledge is the kind of knowledge that is difficult to transfer to another person by means of writing it down or verbalizing it. In the mineral grinding process, the proficiency of the operators depends on the tacit knowledge gained from their experience and training rather than on knowledge learned from a handbook. This article proposed a method combining the electroencephalogram (EEG) signals and the industrial process to detect the proficiency of the operators in the mineral grinding process to reveal the effect of tacit knowledge on the functional cortical connection. The functional brain networks of operators were established based on partial direct coherence and directed transfer function of EEG, and the multi-classifiers were used with the graph-theoretic indexes of the FBNs as input to distinguish the trained operators (Hps) from the non-trained operators (Lps). The results showed that the brain networks of Hps had a better connectivity than those of Lps (p < 0.01), and the accuracy of classification was up to 94.2%. Our studies confirm that based on the performance of EEG features and the combination of industrial operational operation and cognitive processes, the proficiency of the operators can be detected.

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“…We assume a single dipole, located in the center of the ROI, to represent the EEG source of the whole ROI. This restriction may be relaxed using a cortical patch basis model (Cheung et al 2009, Limpiti et al 2006. In the simulations we use the template head model from spm8 (http://www.fil.ion.ucl.ac.uk/spm/software/spm8/) together with the boundary element method to calculate the EEG forward model.…”

Section: Application To Eeg Datamentioning

confidence: 99%

Joint EEG/fMRI state space model for the detection of directed interactions in human brains—a simulation study

Lenz¹,

Musso²,

Linke³

et al. 2011

Physiol. Meas.

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An often addressed challenge in neuroscience research is the assignment of different tasks to specific brain regions. In many cases several brain regions are activated during a single task. Therefore, one is also interested in the temporal evolution of brain activity to infer causal relations between activated brain regions. These causal relations may be described by a directed, task specific network which consists of activated brain regions as vertices and directed edges. The edges describe the causal relations. Inference of the task specific brain network from measurements like electroencephalography (EEG) or functional magnetic resonance imaging (fMRI) is challenging, due to the low spatial resolution of the former and the low temporal resolution of the latter. Here, we present a simulation study investigating a possible combined analysis of simultaneously measured EEG and fMRI data to address the challenge specified above. A nonlinear state space model is used to distinguish between the underlying brain states and the (simulated) EEG/fMRI measurements. We make use of a modified unscented Kalman filter and a corresponding unscented smoother for the estimation of the underlying neural activity. Model parameters are estimated using an expectation-maximization algorithm, which exploits the partial linearity of our model. Inference of the brain network structure is then achieved using directed partial correlation, a measure for Granger causality. The results indicate that the convolution effect of the fMRI forward model imposes a big challenge for the parameter estimation and reduces the influence of the fMRI in combined EEG-fMRI models. It remains to be investigated whether other models or similar combinations of other modalities such as, e.g., EEG and magnetoencephalography can increase the profit of the promising idea of combining various modalities.

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State-Space Multivariate Autoregressive Models for Estimation of Cortical Connectivity from EEG

Cited by 6 publications

References 17 publications

Estimation of Cortical Connectivity From EEG Using State-Space Models

Estimation of Cortical Connectivity From EEG Using State-Space Models

Study of Human Tacit Knowledge Based on Electroencephalogram Signal Characteristics

Joint EEG/fMRI state space model for the detection of directed interactions in human brains—a simulation study

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