# Width of the transition band when applying a Hilbert transform **URL:** \ **Category:** Mailing List Archive (read-only)\ **Tags:** list-archive\ **Created:** [January 20, 2018, 10:45pm UTC](https://mne.discourse.group/t/width-of-the-transition-band-when-applying-a-hilbert-transform/1403 "2018-01-20T22:45:46Z")\ **Posts on this page:** 9\ **Page:** 1
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [January 20, 2018, 10:45pm UTC](https://mne.discourse.group/t/width-of-the-transition-band-when-applying-a-hilbert-transform/1403/1 "2018-01-20T22:45:46Z")
Hello, My question relates more to a signal processing issue, so apologies in advance if it is not appropriate for the forum. I am currently doing an exploratory EEG power analysis using the filter-Hilbert transform and would appreciate some advice on the parameterization of the band-pass filter. For each epoch and time step, I want to compute the power of each frequency from 1 to 30 Hz (in steps of 1). I am thus bandpass filtering the data prior to the application of the Hilbert transform (so I bandpass filter from 1 to 2Hz, then from 2 to 3hz etc. ). Mne's FIR filter implemented via mne.filter.filter\_data is perfect for this purpose, but I am wondering if I should use the default automatic width of transition band, or whether i should set l\_trans\_bandwidth and h\_trans\_bandwidth to some fixed value x across all frequency bands. If so, how should I set x to achieve a good balance between frequency/timing precision ? Best, J. -------------- next part -------------- An HTML attachment was scrubbed... URL: [http://mail.nmr.mgh.harvard.edu/pipermail/mne\_analysis/attachments/20180120/c5561245/attachment.html](http://mail.nmr.mgh.harvard.edu/pipermail/mne_analysis/attachments/20180120/c5561245/attachment.html) ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [January 22, 2018, 8:17pm UTC](https://mne.discourse.group/t/width-of-the-transition-band-when-applying-a-hilbert-transform/1403/2 "2018-01-22T20:17:51Z")
hi J, may I ask what you are aiming to do afterwards with these filtered data? Alex ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [January 22, 2018, 8:27pm UTC](https://mne.discourse.group/t/width-of-the-transition-band-when-applying-a-hilbert-transform/1403/3 "2018-01-22T20:27:45Z")
Hello, I mainly want to estimate theta, alpha, and beta power in the 200-600ms postimulus window. Note that I could bandpass filter the data in the corresponding ranges (e.g., theta: 4-7). averaged power in a specific temporal window (200 600ms poststimulus) for theta bvand, alpha band, and beta band. ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [January 22, 2018, 8:30pm UTC](https://mne.discourse.group/t/width-of-the-transition-band-when-applying-a-hilbert-transform/1403/4 "2018-01-22T20:30:22Z")
I would fix the transition band widths otherwise you will have different filters per band. In one of our examples we do something similar. Denis ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [January 22, 2018, 8:31pm UTC](https://mne.discourse.group/t/width-of-the-transition-band-when-applying-a-hilbert-transform/1403/5 "2018-01-22T20:31:03Z")
hi, how about starting with something like this: [http://martinos.org/mne/stable/auto\_examples/time\_frequency/plot\_time\_frequency\_global\_field\_power.html](http://martinos.org/mne/stable/auto_examples/time_frequency/plot_time_frequency_global_field_power.html) ? HTH Alex ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [January 22, 2018, 8:32pm UTC](https://mne.discourse.group/t/width-of-the-transition-band-when-applying-a-hilbert-transform/1403/6 "2018-01-22T20:32:33Z")
Yes, this is the example I had in mind. ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [January 22, 2018, 11:11pm UTC](https://mne.discourse.group/t/width-of-the-transition-band-when-applying-a-hilbert-transform/1403/7 "2018-01-22T23:11:58Z")
Hi J, Note that 1 Hz wide filters are very narrow, especially if you want to analyze only a 400 ms window. The higher your frequency resolution, the lower your temporal resolution. Best, Ross ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [January 23, 2018, 2:46pm UTC](https://mne.discourse.group/t/width-of-the-transition-band-when-applying-a-hilbert-transform/1403/8 "2018-01-23T14:46:16Z")
Hi Ross, I agree. I set the transition band to 2 Hz, but I don't know if that will provide sufficient temporal resolution. Is there some analytic way to determine the temporal/spectral precision based on the width of the transition band? Best, J ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [January 23, 2018, 7:42pm UTC](https://mne.discourse.group/t/width-of-the-transition-band-when-applying-a-hilbert-transform/1403/9 "2018-01-23T19:42:51Z")
Unfortunately there's no hard and fast rule. In general you should filter as "gently" as possible, since impulse responses can get shockingly long with steeper slopes in the frequency domain. The best way to know how spread in time your filtered signal is going to be is probably to look directly at the filter impulse response (or the envelope of the IR). That will give you and idea of how much the estimate at one time point depends on the time points nearby. Another good thing to do is to make some synthetic, known input signals and process them to see how your analysis affects them. Hope that helps, Ross