[Eeglablist] causal bandpassfilter with very low cutoff

[Davide Baldo]

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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