[Mne_analysis] Low pass filtering questions

[Alexandre Gramfort]

hi Don,

MNE uses what is called an overlap-add approach. Indeed fft is applied to
buffers of 2048 samples by default. The filter is applied in the
frequency domain
using a transition band of 5Hz. The benefit is that it is much faster
and you only
need to store a fraction of the data in memory. FYI the MNE-Python code
uses it too but also supports IIR filters.

HTH
Alex

On Tue, Mar 12, 2013 at 4:19 PM, Krieger, Donald N. <kriegerd at upmc.edu> wrote:
> Hi Everyone,
>
>
>
> I use the low pass filtering build into mne_process_raw().
>
> By default, it appears to use a window of 2048 points and a taper of 5 Hz.
>
>
>
> I would like to make sure I understand what the filter is doing.
>
> I assume that these mean the following:
>
>
>
> 2048 points at a time are run through an FFT.
>
> A cosine^2 window is applied to the fourier coefficients beginning at a
> point where, close to the selected ½ amplitude point, for the entire set of
> coefficients?
>
> Or perhaps the fourier coefficients are tapered down to zero over a 5 hz
> interval?
>
> I’m definitely hazy on this part.
>
>
>
> The resulting “filtered” fourier coefficients are then run through an
> inverse fourier transform to replace the original time domain signal with a
> low pass filtered one.
>
>
>
> What is the correct understanding of this?
>
> And is there information about the transfer function of the filter?
>
>
>
> For instance, what is the fall off per octave depending on the selected ½
> amplitude point and width of the taper?
>
>
>
> Thanks for any help you can provide.
>
>
>
> Regards,
>
>
>
> Don
>
>
>
> Don Krieger, Ph.D.
>
> Department of Neurological Surgery
>
> University of Pittsburgh
>
>
>
>
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