[Eeglablist] causal bandpassfilter with very low cutoff

[Makoto Miyakoshi]

Dear Davide,

> I don't understand why to use a IIR filter. They always cause some (phase) distorsion.

Is it true even with filtfilt?

Makoto

2012/8/8 Davide Baldo <davidebaldo84 at gmail.com>:
> Hi!
>
> I don't understand why to use a IIR filter. They always cause some (phase)
> distorsion.
> 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.
> 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).
>
> Now you are ready to filter your data:
>
>     HP_Delay = round( mean(grpdelay(HP_filter)) ); % a FIR filter introduces
> a delay in the signal. you need to compensate it.
>
>     HP_Data = filter( HP_filter, 1, your_data ) ;  % Filter the data
>     HP_Data  = circshift(  HP_Data , [1 -HP_Delay] ); %compensate the delay
> introduced by the HP filter
>
> I hope it helps you.
>
> Ciao!
>
> Davide.
>
> On Mon, Aug 6, 2012 at 6:38 PM, Andreas Widmann <widmann at uni-leipzig.de>
> wrote:
>>
>> Hi Sophie,
>>
>> 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.
>>
>> 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!).
>>
>> 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).
>> E.g.:
>> >> [b, a] = butter(4, 0.1 / (srate / 2), 'high')
>> for a forth order filter.
>>
>> Check frequency response with
>> >> freqz(b, a, 2^14, srate)
>> 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.
>>
>> Check filter stability with
>> >> zplane(b, a)
>> 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.
>>
>> 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.
>>
>> Hope this helps, best,
>> Andreas
>>
>> Am 06.08.2012 um 17:44 schrieb Sophie Herbst <ksherbst at googlemail.com>:
>>
>> > Hi EEGlablist,
>> >
>> > 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)
>> > by using iirfilt.m:
>> >
>> > iirfilt(EEG.data, EEG.srate, 0.01, 40, 0, 0, 0, [], [], 'on')
>> >
>> > 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
>> > the filter runs but leaves a matrix of NaNs.
>> >
>> > I have been playing around with values for the transition bandwidth etc,
>> > but I could not get a satisfying frequency response.
>> > Any idea why this is and which filter would be better to use?
>> >
>> > Thanks a lot,
>> > Sophie
>>
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>
>
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-- 
Makoto Miyakoshi
JSPS Postdoctral Fellow for Research Abroad
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego