EEG source localization based on multivariate autoregressive models using Kalman filtering

Padilla-Buriticá, Jorge Ivan; Giraldo, Eduardo; Castellanos-Domínguez, G.

doi:10.1109/iembs.2011.6091807

“…The results show that estimating multivariate autoregressive (MVAR) models improves the quality of inverse solutions to a significant degree compared to immediate conventional 7 solutions. However, these conditions are true when the regularized inverse of Tikhonov is used [62]. Recently, variation-based sparse cortical current density (VB-SCCD) algorithm, which extracts the sparsity of the variational map of the sources, has been considered as a promising approach in comparison to source imaging techniques.…”

Section: Detection Of Epileptic Seizures By Brain Activity Localizatimentioning

confidence: 99%

A systematic review of EEG source localization techniques and their applications on diagnosis of brain abnormalities

Asadzadeh

¹

,

Rezaii

²

,

Beheshti

³

et al. 2020

Journal of Neuroscience Methods

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In recent years, multiple noninvasive imaging modalities have been used to develop a better understanding of the human brain functionality, including positron emission tomography, single-photon emission computed tomography, and functional magnetic resonance imaging, all of which provide brain images with millimeter spatial resolutions. Despite good spatial resolution, time resolution of these methods are poor and values are about seconds. Electroencephalography (EEG) is a popular non-invasive electrophysiological technique of relatively very high time resolution which is used to measure electric potential of brain neural activity. Scalp EEG recordings can be used to perform the inverse problem in order to specify the location of the dominant sources of the brain activity. In this paper, EEG source localization research is clustered as follows: solving the inverse problem by statistical method (37.5%), diagnosis of brain abnormalities using common EEG source localization methods (18.33%), improving EEG source localization methods by non-statistical strategies (3.33%), investigating the effect of the head model on EEG source imaging results (12.5%), detection of epileptic seizures by brain activity localization based on EEG signals (20%), diagnosis and treatment of ADHD abnormalities (8.33%). Among the available methods, minimum norm solution has shown to be very promising for sources with different depths. This review investigates diseases that are diagnosed using EEG source localization techniques. In this review we provide enough evidence that the effects of psychiatric drugs on the activity of brain sources have not been enough investigated, which provides motivation for consideration in the future research using EEG source localization methods.Index Terms-EEG signals, source localization, the inverse problem, head model, brain abnormalities, time resolution.S. Beheshti is with the

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“…Their approach to build such a model is proposed on the basis of the multivariate empirical orthogonal functions method. Padilla-Buritica ´et al (2011) considered multivariate autoregressive models for the estimation of the brain activity from electroencephalographic (EEG) time series. They used Kalman filtering to estimate the source dynamics between the EEG and the neural activity into the brain which can be computed using Maxwell equations.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Kalman filtering for spatio-temporal processes

Ferreira

¹

,

Mateu

²

,

Porcu

³

2022

Stoch Environ Res Risk Assess

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An increasing interest in models for multivariate spatio-temporal processes has been noted in the last years. Some of these models are very flexible and can capture both marginal and cross spatial associations amongst the components of the multivariate process. In order to contribute to the statistical analysis of these models, this paper deals with the estimation and prediction of multivariate spatio-temporal processes by using multivariate state-space models. In this context, a multivariate spatio-temporal process is represented through the well-known Wold decomposition. Such an approach allows for an easy implementation of the Kalman filter to estimate linear temporal processes exhibiting both short and long range dependencies, together with a spatial correlation structure. We illustrate, through simulation experiments, that our method offers a good balance between statistical efficiency and computational complexity. Finally, we apply the method for the analysis of a bivariate dataset on average daily temperatures and maximum daily solar radiations from 21 meteorological stations located in a portion of south-central Chile.

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“…Their approach to build such a model is proposed on the basis of the multivariate empirical orthogonal functions method. [33] considered multivariate autoregressive models for the estimation of the brain activity from electroencephalographic (EEG) time series. They used Kalman filtering to estimate the source dynamics between the EEG and the neural activity into the brain which can be computed using Maxwell equations.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Kalman Filtering for Spatio-Temporal Processes

Ferreira

¹

,

Mateu

²

,

Porcu

³

et al. 2021

Preprint

0

View full text Add to dashboard Cite

An increasing interest in models for multivariate spatio-temporal processes has been noted in the last years. Some of these models are very flexible and can capture both marginal and cross spatial associations amongst the components of the multivariate process. In order to contribute to the statistical analysis of these models, this paper deals with the estimation and prediction of multivariate spatio-temporal processes by using multivariate state-space models. In this context, a multivariate spatio-temporal process is represented through the well-known Wold decomposition. Such an approach allows for an easy implementation of the Kalman filter to estimate linear temporal processes exhibiting both short and long range dependencies, together with a spatial correlation structure. We illustrate, through simulation experiments, that our method offers a good balance between statistical efficiency and computational complexity. Finally, we apply the method for the analysis of a bivariate dataset on average daily temperatures and maximum daily solar radiations from 21 meteorological stations located in a portion of south-central Chile.

show abstract

EEG source localization based on multivariate autoregressive models using Kalman filtering

Cited by 5 publications

References 10 publications

A systematic review of EEG source localization techniques and their applications on diagnosis of brain abnormalities

A systematic review of EEG source localization techniques and their applications on diagnosis of brain abnormalities

Multivariate Kalman filtering for spatio-temporal processes

Multivariate Kalman Filtering for Spatio-Temporal Processes

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