Medicine (St Vincent's) - Research Publications

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    A circle criterion observer for estimating the unmeasured membrane potential of neuronal populations
    Chong, M ; Postoyan, R ; Nešić, D ; Kuhlmann, L ; Varsavsky, A (IEEE, 2011-12-01)
    A circle criterion observer is designed for estimating the unmeasured membrane potential of neuronal populations using the electroencephalogram (EEG) from a class of parameterised models that replicates patterns seen on the EEG. Compared to existing similar designs, we provide a less conservative linear matrix inequality (LMI) condition that is shown to be fulfilled for the neural models we consider. The designed observer is robust towards disturbances in the input and measurement, as well as model uncertainty. We show that the observer can be designed for a model that reproduces alpha rhythms in the EEG as an illustrative example.
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    Parameter and state estimation for a class of neural mass models
    Postoyan, R ; Chong, M ; Nesic, D ; Kuhlmann, L (IEEE, 2012-01-01)
    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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    PATIENT-SPECIFIC NEURAL MASS MODELING - STOCHASTIC AND DETERMINISTIC METHODS
    Freestone, DR ; Kuhlmann, L ; Chong, MS ; Nesic, D ; Grayden, DB ; Aram, P ; Postoyan, R ; CooK, MJ ; Tetzlaff, R ; Elger, CE ; Lehnertz, K (WORLD SCIENTIFIC PUBL CO PTE LTD, 2013)
    Deterministic and stochastic methods for online state and parameter estimation for neural mass models are presented and applied to synthetic and real seizure electrocorticographic signals in order to determine underlying brain changes that cannot easily be measured. The first ever online estimation of neural mass model parameters from real seizure data is presented. It is shown that parameter changes occur that are consistent with expected brain changes underlying seizures, such as increases in postsynaptic potential amplitudes, increases in the inhibitory postsynaptic time-constant and decreases in the firing threshold at seizure onset, as well as increases in the firing threshold as the seizure progresses towards termination. In addition, the deterministic and stochastic estimation methods are compared and contrasted. This work represents an important foundation for the development of biologically-inspired methods to image underlying brain changes and to develop improved methods for neurological monitoring, control and treatment.
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    A nonlinear estimator for the activity of neuronal populations in the hippocampus
    Chong, M ; Postoyan, R ; Nešić, D ; Kuhlmann, L ; Varsavsky, A (IFAC - International Federation of Automatic Control, 2011-01-01)
    We present an estimator design to reconstruct the mean membrane potential of individual neuronal populations from a single channel simulated electroencephalographic signal based on a model of the hippocampus. The robustness of the estimator against variations in the synaptic gains of the neuronal populations and disturbances in the input and measurement is studied. Our results are further illustrated in simulations.
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    Estimating the unmeasured membrane potential of neuronal populations from the EEG using a class of deterministic nonlinear filters
    Chong, M ; Postoyan, R ; Nesic, D ; Kuhlmann, L ; Varsavsky, A (IOP PUBLISHING LTD, 2012-04)
    We present a model-based estimation method to reconstruct the unmeasured membrane potential of neuronal populations from a single-channel electroencephalographic (EEG) measurement. We consider a class of neural mass models that share a general structure, specifically the models by Stam et al (1999 Clin. Neurophysiol. 110 1801-13), Jansen and Rit (1995 Biol. Cybern. 73 357-66) and Wendling et al (2005 J. Clin. Neurophysiol. 22 343). Under idealized assumptions, we prove the global exponential convergence of our filter. Then, under more realistic assumptions, we investigate the robustness of our filter against model uncertainties and disturbances. Analytic proofs are provided for all results and our analyses are further illustrated via simulations.
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    A robust circle criterion observer with application to neural mass models
    Chong, M ; Postoyan, R ; Nesic, D ; Kuhlmann, L ; Varsavsky, A (PERGAMON-ELSEVIER SCIENCE LTD, 2012-11-01)
    A robust circle criterion observer is designed and applied to neural mass models. At present, no existing circle criterion observers apply to the considered models, i.e. the required linear matrix inequality is infeasible. Therefore, we generalise available results to derive a suitable estimation algorithm. Additionally, the design also takes into account input uncertainty and measurement noise. We show how to apply the observer to estimate the mean membrane potential of neuronal populations of a popular single cortical column model from the literature.