Hi again,<br>thank you for your very helpful comments. Andreas, I was trying to implement your suggestion but can't quite get it to work.<br>After low-pass filtering (30Hz), downsampling (128Hz), the butterworth filter you suggested transforms large parts of the data into NaNs. <br>
I think it might already go wrong with the low-pass filter. Do you have a suggestion what to use here? It should also be causal, so I tried both iirfilt and butter but apparently I was not able to put together the right combination of parameters.<br>
<br>Sorry for being so ignorant but my understanding of filtering is at a very early stage. <br>Sophie<br><br><br>
<br><br><div class="gmail_quote">On Wed, Aug 8, 2012 at 12:13 PM, Andreas Widmann <span dir="ltr"><<a href="mailto:widmann@uni-leipzig.de" target="_blank">widmann@uni-leipzig.de</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
Hi,<br>
<br>
Sophie asked for a *causal* filter. IIR is an appropriate filter type for this purpose. Using a linear phase FIR filter, as suggested, one will introduce a long filter delay. Using a minimum-phase FIR filter one will loose linear phase anyway. Thus, it is reasonable to use IIR as the filter delay is usually considerably smaller than for minimum-phase FIR in this type of application. (I have a slight preference for Butterworth due to its good (flat) frequency response in the passband.)<br>
<br>
See also recent contribution on causal filtering by Rousselet:<br>
<a href="http://www.frontiersin.org/Perception_Science/10.3389/fpsyg.2012.00131/full" target="_blank">http://www.frontiersin.org/Perception_Science/10.3389/fpsyg.2012.00131/full</a><br>
and my short comment (mainly focused on why one should not use the EEGLAB Basic FIR filter)<br>
<a href="http://www.frontiersin.org/Perception_Science/10.3389/fpsyg.2012.00233/full" target="_blank">http://www.frontiersin.org/Perception_Science/10.3389/fpsyg.2012.00233/full</a><br>
<br>
Best,<br>
Andreas<br>
<br>
Am 08.08.2012 um 10:17 schrieb Davide Baldo <<a href="mailto:davidebaldo84@gmail.com">davidebaldo84@gmail.com</a>>:<br>
<br>
> Hi!<br>
><br>
> I don't understand why to use a IIR filter. They always cause some (phase) distorsion.<br>
> I suggest you to use a FIR filter. If you use Matlab, just type "fdatool" (Filter design & analysis tool). Than select: High pass and FIR (Equiripple). Set the Srate to 512 and the Fstop to 0.01. You did not specfied which frequencies you do not want to distort. I guess you could set Fpass to 0.5 Hz (all frequencies higher than 0.5 Hz won't be modified). Then set Dstop to 0.0005 and Dpass to 0.01.<br>
> Now you can click on Design Filter. When done...click on File -> Export (It export the filter on Matlab workspace). Set Numerator to "HP_Filter" (it s just a name for the filter).<br>
><br>
> Now you are ready to filter your data:<br>
><br>
> HP_Delay = round( mean(grpdelay(HP_filter)) ); % a FIR filter introduces a delay in the signal. you need to compensate it.<br>
><br>
> HP_Data = filter( HP_filter, 1, your_data ) ; % Filter the data<br>
> HP_Data = circshift( HP_Data , [1 -HP_Delay] ); %compensate the delay introduced by the HP filter<br>
><br>
> I hope it helps you.<br>
><br>
> Ciao!<br>
><br>
> Davide.<br>
<div><div class="h5">><br>
> On Mon, Aug 6, 2012 at 6:38 PM, Andreas Widmann <<a href="mailto:widmann@uni-leipzig.de">widmann@uni-leipzig.de</a>> wrote:<br>
> Hi Sophie,<br>
><br>
> a 0.01 Hz highpass filter with 512 Hz sampling frequency is very extreme. My personal rule of thumb is that the srate / cutoff ratio for IIR highpass filtering should not be much higher than ~1000 (acknowledgement to BM). The problem is increased by the EEGLAB default estimation of required filter order by a very narrow transition band (defined as cutoff/3 in your case; the help text in pop_iirfilt is wrong!). The extreme filter cutoff with narrow transition band requires a high filter order (here 6), but the resulting 6th order filter is instable.<br>
><br>
> I would suggest first downsampling the data to an as low as possible sampling frequency (after lowpass filtering the data to 1/4-1/5 of new sampling frequency!).<br>
><br>
> Then, I would suggest filtering the data with a butterworth filter at 0.1 Hz cutoff frequency. Roll-off is a function of filter order (approx. order times -6dB/octave).<br>
> E.g.:<br>
> >> [b, a] = butter(4, 0.1 / (srate / 2), 'high')<br>
> for a forth order filter.<br>
><br>
> Check frequency response with<br>
> >> freqz(b, a, 2^14, srate)<br>
> At 256 Hertz this gives a reasonable frequency response and very good DC attenuation. Downsampling your data to 128 Hz you can use the same filter for a 0.05 Hz highpass.<br>
><br>
> Check filter stability with<br>
> >> zplane(b, a)<br>
> All poles should be inside the unit circle. If you test the 6th order butterworth filter you will see that also this filter is instable.<br>
><br>
> Causal filtering can be done easily on the command line using the MATLAB built in filter function. Take care not to filter across boundaries/DC offsets! Filter each segment separately.<br>
><br>
> Hope this helps, best,<br>
> Andreas<br>
><br>
> Am 06.08.2012 um 17:44 schrieb Sophie Herbst <<a href="mailto:ksherbst@googlemail.com">ksherbst@googlemail.com</a>>:<br>
><br>
> > Hi EEGlablist,<br>
> ><br>
> > I am trying to apply a causal bandpass filter with a very low cutoff (0.01Hz) to my EEG data (continuos data with ~2,700,000 points, srate = 512Hz)<br>
> > by using iirfilt.m:<br>
> ><br>
> > iirfilt(EEG.data, EEG.srate, 0.01, 40, 0, 0, 0, [], [], 'on')<br>
> ><br>
> > I seem to run into similar problems as described in EEGLAB Bug #1011: the default values for transition bandwidth and passband/ stopband ripple do not seem to work as<br>
> > the filter runs but leaves a matrix of NaNs.<br>
> ><br>
> > I have been playing around with values for the transition bandwidth etc, but I could not get a satisfying frequency response.<br>
> > Any idea why this is and which filter would be better to use?<br>
> ><br>
> > Thanks a lot,<br>
> > Sophie<br>
><br>
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