A state space approach to multimodal integration of simultaneously recorded EEG and fMRI

Purdon, Patrick L.; Lamus, Camilo; Hämäläinen, Matti S.; Brown, Emery N.

doi:10.1109/icassp.2010.5494906

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…The time resolution of fMRI observations is much lower than that of EEG observations, with typically one datapoint every 2 sec. In principle, this setup could be modeled by directly fusing both signals using a time-varying observation matrix integrating EEG and fMRI into a single-observation model (Purdon et al, 2010). Moreover, it would also be possible to directly integrate BOLD-derived inequality constraints by using constrained Kalman filtering approaches (Simon and Chia, 2002;Simon and Simon, 2003) or to extend approaches for the slow-fast systems.…”

Section: Using the Virtual Brainmentioning

confidence: 99%

The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging

et al. 2013

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Brain function is thought to emerge from the interactions among neuronal populations. Apart from traditional efforts to reproduce brain dynamics from the micro-to macroscopic scales, complementary approaches develop phenomenological models of lower complexity. Such macroscopic models typically generate only a few selected-ideally functionally relevant-aspects of the brain dynamics. Importantly, they often allow an understanding of the underlying mechanisms beyond computational reproduction. Adding detail to these models will widen their ability to reproduce a broader range of dynamic features of the brain. For instance, such models allow for the exploration of consequences of focal and distributed pathological changes in the system, enabling us to identify and develop approaches to counteract those unfavorable processes. Toward this end, The Virtual Brain (TVB) (www.thevirtualbrain.org), a neuroinformatics platform with a brain simulator that incorporates a range of neuronal models and dynamics at its core, has been developed. This integrated framework allows the modelbased simulation, analysis, and inference of neurophysiological mechanisms over several brain scales that underlie the generation of macroscopic neuroimaging signals. In this article, we describe how TVB works, and we present the first proof of concept.

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Section: Using the Virtual Brainmentioning

confidence: 99%

The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging

et al. 2013

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“…Modeldriven symmetric fusion seeks to model the shared but unobserved neural states that give rise to EEG and fMRI recordings. For example, several studies have employed state-space models to link the temporal evolution of latent neural dynamics with joint EEG and fMRI observations via their own biophysical generative processes (Daunizeau et al 2007, Deneux & Faugeras 2010, Jun et al 2008, Lenz et al 2011, Purdon et al 2010, Riera et al 2006, Rosa et al 2010a, Valdes-Sosa et al 2009. This type of model aims at making inferences directly on the shared latent brain states given EEG and fMRI observations.…”

Section: Introductionmentioning

confidence: 99%

Inferring Macroscale Brain Dynamics via Fusion of Simultaneous EEG-fMRI

Philiastides

Sajda

2021

Annu. Rev. Neurosci.

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Advances in the instrumentation and signal processing for simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI) have enabled new ways to observe the spatiotemporal neural dynamics of the human brain. Central to the utility of EEG-fMRI neuroimaging systems are the methods for fusing the two data streams, with machine learning playing a key role. These methods can be dichotomized into those that are symmetric and asymmetric in terms of how the two modalities inform the fusion. Studies using these methods have shown that fusion yields new insights into brain function that are not possible when each modality is acquired separately. As technology improves and methods for fusion become more sophisticated, the future of EEG-fMRI for noninvasive measurement of brain dynamics includes mesoscale mapping at ultrahigh magnetic resonance fields, targeted perturbation-based neuroimaging, and using deep learning to uncover nonlinear representations that link the electrophysiological and hemodynamic measurements. Expected final online publication date for the Annual Review of Neuroscience, Volume 44 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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“…Lenz et al [7] made use of a modified unscented Kalman filter and a corresponding unscented smoother for the estimation of the underlying neural activity of the brain. Purdon et al [11] have developed a state space approach for mult imodal integration of simu ltaneous EEG and fMRI. Li et al [8] evaluated a new robust tracking algorith m for estimat ing blood pressure and heart rate (HR) based upon a Kalman Filter with an update sequence modified by the KF innovation sequence and the value of the Signal Quality Index (SQI).…”

Section: Introductionmentioning

confidence: 99%

Modeling Epileptic EEG Time Series by State Space Model and Kalman Filtering Algorithm

Goshvarpour¹,

Goshvarpour²,

Shamsi³

2014

IJISA

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The human brain is one of the most complex physiological systems. Therefore, electroencephalogram (EEG) signal modeling is important to achieve a better understanding of the physical mechanis ms generating these signals. The aim of this study is to investigate the application of Kalman filter and the state space model for estimat ion of electroencephalogram signals in a specific pathological state. For this purpose, two types of EEG signals (normal and partial ep ilepsy) were analyzed. The estimation performance of the proposed method on EEG signals is evaluated using the root mean square (RMS) measurement. The result of the present study shows that this model is appropriate for the analysis of EEG recordings. In fact, this model is capable of p redicting changes in EEG time series with phenomena such as epileptic spikes and seizures.

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A state space approach to multimodal integration of simultaneously recorded EEG and fMRI

Cited by 3 publications

References 9 publications

The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging

The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging

Inferring Macroscale Brain Dynamics via Fusion of Simultaneous EEG-fMRI

Modeling Epileptic EEG Time Series by State Space Model and Kalman Filtering Algorithm

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