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High-pass Filtering

High pass filtering is used to remove the worst of the low frequency noise in the FMRI time series. Here we are using the non-linear high-pass filtering discussed in the previous section. In figure 3(b) a FWHM of 40 scans was used and has the effect of removing the lower peak and pushing the whole histogram to higher values. This shift to higher $ S_{\rho }$ values represents a decrease in the positive autocorrelation in the data; this corresponds to a decrease in the parameter variances, as desired. The use of high-pass filtering reduces the low frequency noise and non-stationarity of the time series, making the estimation of the autocorrelation more robust and valid. However, having used a non-linear high-pass filter, the power spectral density of the time series has been shaped in such a way that it is not easily modelled by a low order parametric AR model.



Mark Woolrich 2001-07-16