… and the model fit parameter so we end up with 9 parameters estimated from 2000 digital
EEG signal values (10 s, 200-Hz sampling frequency). The corresponding AR model ( p > 9) …

… previously on modeling of EEG signals via fBm [43], the modeling has been restricted to
1st-order modeling. This study has extended this modeling to the rhythms of EEG signals and it …

… EEG signal model based on MPA where some model parameters are determined adaptively
using the least mean square (LMS) algorithm [17]. The model … the MPA model parameters. …

… Automated analysis and classification of EEG signals is hampered by the unwieldy amounts
… analysis of EEG signals which is based on a hierarchy of models. These models include 1) …

… But the AR model obtains a worse result; only about 85% of the … the AR model is simpler
and obtains better results in our study, the AR model could be a better selection for EEG signal …

… structure of EEG signals is to introduce nonlinear models and higher … model with those
obtained from a bilinear model. It is shown that in certain cases, the nonlinearity of EEG signals is …

… Table 1 gives an overview of signal generation models used in this … models can be divided
into three main families: (i) models of coupled stochastic signals (M1 and M2), (ii) models of …

… of the significance of EEG signal analysis and processing (… of EEG signals; an exploration
of normal and abnormal EEGs, … the EEGs; reviews of theoretical approaches in EEG modelling…

… model is fitted to a sampled signal. If the model forms a good approximation … For such
signals, either an adaptive model can be used, or the signal can be divided into sufficiently short, …

… models to produce any possible activity of EEG signals; but at least, some common activities
must be produced by the model… models of EEG signals and also specialized seizure models …