Analysis and Detection of Neural Synchrony in the Prefrontal Cortex

Signal Analysis techniques are routinely used in Neuroscience to interpret raw signals harvested from the Nervous System. From a simple Fourier analysis to more complicated methods such as multiresolution wavelet analysis, such techniques must be used for signal manipulation in order to reach informed conclusions on the measurements taking place...

Finite-Sample Bias Propagation in Autoregressive Estimation With the Yule–Walker Method

The Yule-Walker (YW) method for autoregressive (AR) estimation uses lagged-product (LP) autocorrelation estimates to compute an AR parametric spectral model. The LP estimates only have a small triangular bias in the estimated autocorrelation function and are asymptotically unbiased. However, using them in finite samples with the YW method for AR...

Five Separate Bias Contributions in Time Series Models for Equidistantly Resampled Irregular Data

The use of time series models for irregular data requires resampling of the data on an equidistant grid. Slotted resampling transforms an irregular randomly sampled process into an equidistant signal where data are missing. An approximate maximum-likelihood time series estimator has been developed to estimate the power spectral density and the...

Modified Durbin Method for Accurate Estimation of Moving-Average Models

Spectra with narrow valleys can accurately be described with moving-average (MA) models by using only a small number of parameters. Durbin's MA method uses the estimated parameters of a long autoregressive (AR) model to calculate the MA parameters. Probably all the pejorative remarks on the quality of Durbin's method in the literature are based...