No. 954

Title:

Modelling non-stationary variance in EEG time series by state space GARCH model

Author(s):

Wong K.F.(Graduate Univ. for Advanced Studies);
Galka A.(Institute of Statistical Mathematics);
Yamashita O.(ATR computational Neuroscience Lab.);
Ozaki T.(Institute of Statistical Mathematics)

Key words:

State space model; Kalman filter; frequency decomposition; autoregressive model; conditional heteroscedasticity; GARCH; non-stationary; EEG; anaesthesia.

Abstract:

    We present a new approach to modeling nonstationarity in EEG time series by a generalized state space approach. A given time series can be decomposed into a set of noise-driven processes, each corresponding to a different frequency band. Nonstationarity is modelled by allowing the variances of the driving noises to change with time, depending on the state prediction error within the state space model. The method is illustrated by an application to EEG data recorded during the onset of anaesthesia.