Estimating the unmeasured membrane potential of neuronal populations from the EEG using a class of deterministic nonlinear filters

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

doi:10.1088/1741-2560/9/2/026001

Cited by 14 publications

(17 citation statements)

References 46 publications

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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.…”

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confidence: 99%

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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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Section: Jansen-rit (Jr) Neural Mass Modelmentioning

confidence: 99%

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confidence: 99%

Neural mass model-based tracking of anesthetic brain states

et al. 2016

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“…As a consequence, we need to modify the observer and the technical proof in [6]. This study extends our previous works [2] and [3] on the state estimation of this class of neural mass models.…”

Section: Introductionmentioning

confidence: 55%

“…Assumption 2: The input u is known. 2 According to the notation of [13] as x 0 = (y 1 0 , y 1 3 , y 1 6 , y 1 7 , . .…”

Section: Problem Formulation and Assumptionsmentioning

confidence: 99%

Parameter and state estimation for a class of neural mass models

Chong¹,

Nešić²,

Kuhlmann³

2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

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We present an adaptive observer which asymptotically reconstructs the parameters and states of a model of interconnected cortical columns. Our study is motivated by the fact that the considered model is able to realistically reproduce patterns seen on (intracranial) electroencephalograms (EEG) by varying its parameters. Therefore, by estimating its parameters and states, we could gain a better understanding of the mechanisms underlying neurological phenomena such as seizures, which might lead to the prediction of the onsets of epileptic seizures. Simulations are performed to illustrate our results.

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“…This modeling approach is initially used in the works of Lopes da Silva et al (1974Silva et al ( , 1976, Van Rotterdam et al (1982), Freeman (1977Freeman ( , 1987 and Eeckman and Freeman (1991) about 40 years ago, and then developed by Jansen et al (1993), Wendling et al (2000Wendling et al ( , 2002 and David et al (2003). Their models have been generally used to study EEG rhythms (Bhattacharya et al 2011), characterize the dynamics of EEG waveforms (Nevado-Holgado et al 2012;Kiebel et al 2008), and especially simulate the dynamic pattern of EEG activity during epilepsy seizures (Chakravarthy et al 2009a;Wendling et al 2000;Chong et al 2012).…”

Section: Introductionmentioning

confidence: 99%

UKF-based closed loop iterative learning control of epileptiform wave in a neural mass model

et al. 2014

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A novel closed loop control framework is proposed to inhibit epileptiform wave in a neural mass model by external electric field, where the unscented Kalman filter method is used to reconstruct dynamics and estimate unmeasurable parameters of the model. Specifically speaking, the iterative learning control algorithm is introduced into the framework to optimize the control signal. In the proposed method, the control effect can be significantly improved based on the observation of the past attempts. Accordingly, the proposed method can effectively suppress the epileptiform wave as well as showing robustness to noises and uncertainties. Lastly, the simulation is carried out to illustrate the feasibility of the proposed method. Besides, this work shows potential value to design model-based feedback controllers for epilepsy treatment.

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Estimating the unmeasured membrane potential of neuronal populations from the EEG using a class of deterministic nonlinear filters

Cited by 14 publications

References 46 publications

Neural mass model-based tracking of anesthetic brain states

Neural mass model-based tracking of anesthetic brain states

Parameter and state estimation for a class of neural mass models

UKF-based closed loop iterative learning control of epileptiform wave in a neural mass model

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