Tracking of Nonstationary Eeg with the Roots of Arma Models
نویسنده
چکیده
The tracking of nonstationary EEG with time-varying ARMA models is discussed. A method for detecting spindles in rat EEG is presented. The method is based on tracking of a single system pole of the ARMA model.
منابع مشابه
Time-Varying ARMA modelling of Nonstationary EEG using Kalman Smoother Algorithm
An adaptive autoregressive moving average (ARMA) modelling of nonstationary EEG by means of Kalman smoother is presented. The main advantage of the Kalman smoother approach compared to other adaptive algorithms such as LMS or RLS is that the tracking lag can be avoided. This advantage is clearly presented with simulations. Kalman smoother is also applied to tracking of alpha band characteristic...
متن کاملTracking of nonstationary EEG with Kalman smoother approach
An adaptive autoregressive moving average (ARMA) modelling of nonstationary EEG by means of Kalman smoother is presented. The main advantage of the Kalman smoother approach compared to other adaptive algorithms such as LMS or RLS is that the tracking lag can be avoided. This advantage is clearly presented with simulations. Kalman smoother is also applied to tracking of alpha band characteristic...
متن کاملNearly Nonstationary Arma Processes: Second Order Properties
Second order properties of nearly nonstationary ARMA processes are investigated in the cases when the autoregressive polynomial equation has (i) a real root close to 1; (ii) a real root close to -1; (iii) a pair of complex roots close to the unit circle. The effect of the closeness to the unit circle of the ARMA poles on its covariance and spectral density functions is considered. The obtained ...
متن کاملThe Stationary - NonStationary Process and The Variable Roots Difference Equations
Stochastic, processes can be stationary or nonstationary. They depend on the magnitude of shocks. In other words, in an auto regressive model of order one, the estimated coefficient is not constant. Another finding of this paper is the relation between estimated coefficients and residuals. We also develop a catastrophe and chaos theory for change of roots from stationary to a nonstationary one ...
متن کاملSome problems in the overspeci cation of ARMA and ARIMA processes using ARFIMA models
Nonstationary ARIMA processes and nearly nonstationary ARMA processes, such as autoregressive processes having a root of the AR polynomial close to the unit circle, have sample autocovariance and spectral properties that are, in practice, almost indistinguishable from those of a stationary longmemory process, such as a Fractionally Integrated ARMA (ARFIMA) process. Because of this, model misspe...
متن کامل