[Eeglablist] Cleanline and Basic FIR filter
Eric HG
erichg2013 at gmail.com
Wed Sep 9 06:23:14 PDT 2015
In addition to the last questions:
4) What should the Max passband deviation be 0.001 or 0.0001?
5) What should the transition bandwith be? 3.6 or 5.0?
Best,
Eric
On Wed, Sep 9, 2015 at 3:07 PM, Eric HG <erichg2013 at gmail.com> wrote:
> Hi Andreas and Makoto,
>
> Thank you very much for your responses! I've been trying to adapt the
> function for windowed sinc FIR filter as you suggested with Kaiser as
> window type (Widmann et al. 2015):
>
> EEG = pop_firws(EEG, 'fcutoff', [1 70], 'ftype', 'bandpass', 'wtype',
> 'kaiser', 'warg', 5.65326, 'forder', 1812, 'minphase', 0);
>
> However, I have some questions about the use:
> 1) Is this the best way to use a FIR filter?
> 2) Is there any way to estimate the "Kaiser window beta" ('warg') without
> using the GUI?
> 3) Is there a way to estimate the filter order ('forder') without using
> the GUI?
>
> Best,
>
> Eric
>
> On Wed, Sep 2, 2015 at 8:02 PM, Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
> wrote:
>
>> Dear Eric,
>>
>> > The only major difference is that you define passband edges in
>> pop_eegfiltnew but half amplitude (-6 dB) cutoffs in the windowed sinc
>> filter.
>>
>> For details, see below.
>> https://cloud.github.com/downloads/widmann/firfilt/firfilt.pdf
>> http://sccn.ucsd.edu/wiki/Firfilt_FAQ
>>
>> Makoto
>>
>> On Sun, Aug 30, 2015 at 11:20 AM, Andreas Widmann <widmann at uni-leipzig.de
>> > wrote:
>>
>>> Hi Eric,
>>>
>>> > I have some questions about both cleanline and the basic FIR filter:
>>> >
>>> > 1) Do you have any recommendation about which one should be used
>>> first? At the moment I'm doing this (cleanline first and then bandpass
>>> filtering):
>>> Highpass filtering should be done before cleanline. Cleanline expects
>>> stationary data. See Nima’s recent paper for a more detailed explanation
>>> (Bigdely-Shamlo N, Mullen T, Kothe C, Su K-M and Robbins KA (2015) The PREP
>>> pipeline: standardized preprocessing for large-scale EEG analysis. Front.
>>> Neuroinform. 9:16. doi: 10.3389/fninf.2015.0001). Consider the „temporary
>>> highpass“-solution suggested there in applications not requiring a highpass
>>> otherwise.
>>>
>>> > [EEG, Sorig, Sclean, f, amps, freqs, g] = pop_cleanline(EEG,
>>> 'Bandwidth',2,'ChanCompIndices',[1:EEG.nbchan], ...
>>> >
>>> 'SignalType','Channels','ComputeSpectralPower',true, ...
>>> > 'LineFrequencies',[50 100]
>>> ,'NormalizeSpectrum',false, ...
>>> >
>>> 'LineAlpha',0.01,'PaddingFactor',2,'PlotFigures',false, ...
>>> >
>>> 'ScanForLines',true,'SmoothingFactor',100,'VerboseOutput',1, ...
>>> >
>>> 'SlidingWinLength',EEG.pnts/EEG.srate,'SlidingWinStep',EEG.pnts/EEG.srate);
>>> >
>>> > EEG = pop_eegfiltnew(EEG, 1, 70, 1650, 0, [], 1);
>>> >
>>> > 2) I'm doing the filtering before epoching the data (on continuous
>>> data). Can that lead to any problem with the filtering?
>>> Filtering should always be done on the continuous data!
>>>
>>> > In cleanline: Both the 'SlidingWinLength' and 'SlidingWinStep' are the
>>> length of the recording. However, the 'SmootingFactor is 100, which
>>> shouldn't matter when the window length = EEG length.
>>> >
>>> > 3) Is there an easy way to figure out the filter order for
>>> pop_eegfiltnew? The current 1650 is just what was decided through the GUI.
>>> Use the windowed sinc FIR filter to manually adjust the filter order.
>>> pop_eegfiltnew is just a front-end for the windowed sinc filter with
>>> hardcoded hamming window and a default heuristic for filter order as
>>> explained in the help text. The only major difference is that you define
>>> passband edges in pop_eegfiltnew but half amplitude (-6 dB) cutoffs in the
>>> windowed sinc filter.
>>>
>>> Hope this helps! Best,
>>> Andreas
>>> _______________________________________________
>>> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
>>> To unsubscribe, send an empty email to
>>> eeglablist-unsubscribe at sccn.ucsd.edu
>>> For digest mode, send an email with the subject "set digest mime" to
>>> eeglablist-request at sccn.ucsd.edu
>>
>>
>>
>>
>> --
>> Makoto Miyakoshi
>> Swartz Center for Computational Neuroscience
>> Institute for Neural Computation, University of California San Diego
>>
>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://sccn.ucsd.edu/pipermail/eeglablist/attachments/20150909/995d0e64/attachment.html>
More information about the eeglablist
mailing list