Parameter and state estimation for a class of neural mass models

Chong, M. S.; Nešić, Dragan; Kuhlmann, Levin

doi:10.1109/cdc.2012.6427031

Cited by 10 publications

(9 citation statements)

References 12 publications

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“…Deterministic observer approaches have also been developed to track states and/or parameters of neural mass models (Chong et al, 2011a; 2011b; 2012a; 2012b; Postoyan et al, 2012; Freestone et al, 2013). Observer-based approaches are more model-specific, and convergence proofs are easier to obtain than for stochastic filtering approaches; however, further work needs to be done to ensure parameter estimation is robust to input and to measurement uncertainties that occur in the practical scenario (Freestone et al, 2013).…”

Section: Model-based Inference Of Physiological Changes Underlying Epmentioning

confidence: 99%

Role of Multiple-Scale Modeling of Epilepsy in Seizure Forecasting

Kuhlmann

Grayden

Wendling

2015

Journal of Clinical Neurophysiology

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Over the past three decades, a number of seizure prediction, or forecasting, methods have been developed. Although major achievements were accomplished regarding the statistical evaluation of proposed algorithms, it is recognized that further progress is still necessary for clinical application in patients. The lack of physiological motivation can partly explain this limitation. Therefore, a natural question is raised: can computational models of epilepsy be used to improve these methods? Here we review the literature on the multiple-scale neural modelling of epilepsy and the use of such models to infer physiological changes underlying epilepsy and epileptic seizures. We argue how these methods can be applied to advance the state-of-the-art in seizure forecasting.

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Section: Model-based Inference Of Physiological Changes Underlying Epmentioning

confidence: 99%

Role of Multiple-Scale Modeling of Epilepsy in Seizure Forecasting

Kuhlmann

Grayden

Wendling

2015

Journal of Clinical Neurophysiology

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“…The JR neural mass model (Jansen & Rit, 1995) of population activity in cerebral cortex, or modifications thereof, forms the basis of many current approaches to infer underlying physiological variables from sparsely sampled electrophysiological recordings (Wendling et al, 2002;Moran et al, 2013;Chong et al, 2011;Postoyan et al, 2012;Chong et al, 2012aChong et al, ,b, 2015Freestone et al, 2011Freestone et al, , 2013Freestone et al, , 2014. This combined with the simplicity of the JR model makes it a suitable first choice in the search for the simplest neural mass model that is both accurate, informative and efficient enough for clinical application in anesthesia.…”

Section: Jansen-rit (Jr) Neural Mass Modelmentioning

confidence: 99%

“…Various approaches for parameter estimation of neural mass models have been developed (Wendling et al, 2002;Moran et al, 2013;Chong et al, 2011;Postoyan et al, 2012;Chong et al, 2012aChong et al, ,b, 2015Freestone et al, 2011Freestone et al, , 2013Freestone et al, , 2014. Given that neural mass models are generally nonlinear and related noise sources can be considered to be Gaussian white noise (Liley et al, 2002;Nunez & Srinivasan, 2006), this paper presents the application of the unscented Kalman filter (UKF) to state and parameter estimation of a neural mass model of population activity in 35 cerebral cortex.…”

mentioning

confidence: 99%

Neural mass model-based tracking of anesthetic brain states

et al. 2016

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Neural mass model-based tracking of brain states from electroencephalographic signals holds the promise of simultaneously tracking brain states while inferring underlying physiological changes in various neuroscientific and clinical applications. Here, neural mass model-based tracking of brain states using the unscented Kalman filter applied to estimate parameters of the Jansen-Rit cortical population model is evaluated through the application of propofol-based anesthetic state monitoring. In particular, 15 subjects underwent propofol anesthesia induction from awake to anesthetised while behavioural responsiveness was monitored and frontal electroencephalographic signals were recorded.The unscented Kalman filter Jansen-Rit model approach applied to frontal electroencephalography achieved reasonable testing performance for classification of the anesthetic brain state (sensitivity: 0.51; chance sensitivity: 0.17; nearest neighbor sensitivity 0.75) when compared to approaches based on linear (autoregressive moving average) modelling (sensitivity 0.58; nearest neighbor sensitivity: 0.91) and a high performing standard depth of anesthesia monitoring measure, Higuchi Fractal Dimension (sensitivity: 0.50; nearest neighbor sensitivity: 0.88). Moreover, it was found that the unscented Kalman filter based parameter estimates of the inhibitory postsynaptic potential amplitude varied in the physiologically expected direction with increases in propofol concentration, while the estimates of the inhibitory postsynaptic potential rate constant did not. These results combined with analysis of monotonicity of parameter estimates, error analysis of parameter estimates, and observability analysis of the Jansen-Rit model, along with considerations of extensions of the Jansen-Rit model, suggests that the Jansen-Rit model combined with unscented Kalman filtering provides a valuable benckmark for future real-time brain state tracking studies. This is especially true for studies of more complex, but still computationally efficient, neural models of anesthesia that can more accurately track the anesthetic brain state, while simultaneously inferring underlying physiological changes that can potentially provide useful clinical information.

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“…In 2012, the authors of [23] presented a method for estimating parameters in the J-R model using state observer. In the next year the method was implemented to estimate parameters of both modeled and real signals (which were obtained via subdural grid electrode) [24].…”

Section: Introductionmentioning

confidence: 99%

A Method to Obtain Parameters of One-Column Jansen–Rit Model Using Genetic Algorithm and Spectral Characteristics

Łysiak

Paszkiel

2021

Applied Sciences

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In this paper, a method of obtaining parameters of one-column Jansen–Rit model was proposed. Methods present in literature are focused on obtaining parameters in an on-line manner, producing a set of parameters for every point in time. The method described in this paper can provide one set of parameters for a whole, arbitrarily long signal. The procedure consists of obtaining specific frequency features, then minimizing mean square error of those features between the measured signal and the modeled signal, using genetic algorithm. This method produces an 8-element vector, which can be treated as an EEG signal feature vector specific for a person. The parameters which were being obtained are maximum postsynaptic potential amplitude, maximum inhibitory potential amplitude, ratio of the number of connections between particular neuron populations, the shape of a nonlinear function transforming the average membrane potential into the firing rate and the input noise range. The method shows high reproducibility (intraclass correlation coefficient for particular parameters ranging from 0.676 to 0.978) and accuracy (ranging from 0.662 to 0.863). It was additionally verified using EEG signal obtained for a single participant. This signal was measured using Emotiv EPOC+ NeuroHeadset.

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Parameter and state estimation for a class of neural mass models

Cited by 10 publications

References 12 publications

Role of Multiple-Scale Modeling of Epilepsy in Seizure Forecasting

Role of Multiple-Scale Modeling of Epilepsy in Seizure Forecasting

Neural mass model-based tracking of anesthetic brain states

A Method to Obtain Parameters of One-Column Jansen–Rit Model Using Genetic Algorithm and Spectral Characteristics

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