[Mne_analysis] Filter settings

Eric Larson larson.eric.d at gmail.com
Thu May 9 08:32:45 EDT 2019
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... and I forgot to mention (thanks Burkhard!) that MNE does a
forward-backward application of the filter. So the order of the filter is
effectively doubled, and the (-3 dB) cutoff frequencies are modified

You can get the impulse response of the filter by doing something like:

signal = np.zeros(10001)
signal[5000] = 1
resp = mne.filter.filter_data(signal, info['sfreq'], 8, 12, ...)

This is the sort of thing done in the filtering tutorial:


Or you can use the `plot_filter` function to do some of this for you:



On Wed, May 8, 2019 at 11:44 AM Eric Larson <larson.eric.d at gmail.com> wrote:

> A question by the reviewer now is how this filter affected the temporal
>> resolution of the eeg signal. My understanding of filtering is very basic,
>> but I believe in addition to the order of the filter this  is largely
>> determined by both the frequency band of interest and the transition
>> bandwiths.
> The idea of a transition band (which separates the pass-band and
> stop-band) is only used for FIR filtering in MNE. IIR filters can be
> defined / reproduced just by specifying the filter type (low-pass,
> high-pass, band-pass, or band-stop), cutoff frequency(ies), order, and
> topology (Bessel, Butterworth, Chebychev w/params, etc.). For you this
> could write something like "we used a 5th order Butterworth band-pass
> filter with cutoff frequencies of 8 and 12 Hz".
> Regarding the resulting temporal resolution, this will relate to both your
> filter characteristics / fall-off, and the width of your pass-band (here, 4
> Hz). The more narrow the pass-band and sharper the filter falloff, the
> worse the resulting temporal resolution will be (and the broader the
> pass-band and shallower the filter slope, the better the resulting temporal
> resolution will be).
> As to how best characterize these things for your reviewer, I'm not sure
> offhand. I can recommend these two articles as likely places where
> best-practices for reporting could give some hints:
> https://www.sciencedirect.com/science/article/pii/S0165027014002866?via%3Dihub
> https://www.cell.com/neuron/fulltext/S0896-6273(19)30174-6
> Let us know if you find something useful in them!
> Eric
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