[Eeglablist] causal bandpassfilter with very low cutoff

Fri Aug 10 04:41:15 PDT 2012

Exactly...simmetric FIR filters has a linear phase response. I also prefer
and have always used FIR filter for the off line processing of EEG data.
Usually equiripple FIR filter.

Thanks for all the answers,

Davide.
Il giorno 10/ago/2012 13:33, "Andreas Widmann" <widmann at uni-leipzig.de> ha
scritto:

> Hi,
>
> I also would strongly prefer linear phase FIR for any kind of non-causal
> filtering.
>
> I'm lacking serious first hand experience with minimum-phase FIR filters
> for causal filtering. Interesting to hear that you would prefer over IIR
> for causal filtering. Will try out. I used IIR for causal filtering so far
> for reasons of comparability as it is implemented in so many other
> electrophysiology software packages and because I like the dB/octave
> property.
>
> @Davide
> FIR filters must have a (anti-)symmetric impulse response to have linear
> phase. Minimum phase FIR filters have an asymmetric impulse response.
>
> @Clemens
> I will send a MATLAB example when I'm back from holiday end of August. If
> I remember correctly the filtfilt 'reflection' algorithm even increases the
> DC artifact at the ends of the signal but I have to re-check whether this
> is valid in general. Without filtfilt one can completely avoid the DC
> artifact by padding the data with the DC constant.
>
> Best,
> Andreas
>
> Am 10.08.2012 um 10:29 schrieb Clemens Brunner <clemens.brunner at tugraz.at
> >:
>
> > Personally, I prefer FIR filters over IIR filters for the following
> reasons:
> > * FIR filters are inherently stable
> > * FIR filters can have linear phase
> > * Minimum-phase FIR filters can have group delays that are comparable or
> even lower than IIR filters
> > * FIR filters are robust to quantization
> > * Most specialized hardware supports only FIR
> >
> > Sophie had problems with stability because she was designing an IIR
> filter. Had she used an FIR filter instead, she could have obtained a
> filter meeting the same specifications, but with guaranteed stability. IIR
> filters are useful when the goal is to minimize implementation costs. Since
> we are working with MATLAB and not with hardware, I don't think that we
> benefit from this property.
> >
> > Anyway, I don't want to start a flame war here, but I think FIR should
> remain the default filter type for EEG processing because of its many
> advantages. For special cases, IIR filters do also make sense. In addition,
> when using non-causal
> >
> > Clemens
> >
> > PS: Regarding filtfilt in MATLAB: of course you can use filtfilt for
> both FIR and IIR filters. Only if you are using the new object-oriented
> filter interface (fdesign or dfilt objects), then filtfilt won't work.
> filtfilt accepts input parameters only in the following forms: filtfilt(b,
> a, x) or filtfilt(sos, g, x).
> >
> > Andreas, could you please elaborate more on the problems when applying
> forward and reverse filtering to signals with DC offsets? I'd appreciate a
> short MATLAB code example.
> >
> >
> >
> >
> > On Aug 9, 2012, at 11:26 PM, Andreas Widmann <widmann at uni-leipzig.de>
> > wrote:
> >
> >> Hi,
> >>
> >> I plan to implement minimum-phase FIR conversion in the firfilt plugin
> (actually it is already written but needs some testing still).
> >>
> >> However, this is for sake of completeness only as minimum-phase FIR
> causal filtering will in most cases be inferior to IIR causal filtering (in
> electrophysiology). Both will have non-linear phase, but IIR has usually
> the smaller filter delay.
> >>
> >> Making filters zero-phase with the MATLAB filtfilt function is
> problematic not only with IIR, as noted by Guillaume, but also with FIR
> filters when large DC offsets are observed (as e.g. in BioSemi DC-recorded
> data). Padding with DC-constant and left-shifting the signal (as
> implemented in firfilt) instead of filtering backward with filtfilt is more
> robust.
> >>
> >> Best,
> >> Andreas
> >>
> >> Am 09.08.2012 um 20:50 schrieb Makoto Miyakoshi <mmiyakoshi at ucsd.edu>:
> >>
> >>> Dear Guillaume and Clemens,
> >>>
> >>>> If you want to do the same procedure with IIR filters, you have to use
> >>>> the filtfiltHD function.
> >>>
> >>> Actually I tested it with Clemens Brunner (who suggested fir1 instead
> >>> of firls, and now fir1 is the default). We confirmed two things.
> >>>
> >>> 1. EEGLAB's IIR filter plug-in uses filtfilt, so it is a zero-phase
> filter.
> >>> 2. EEGLAB's filter option 'causal' does not use a minimum-phase
> >>> filter; this option does not preserve the rise onset, which is
> >>> discussed in Rousselet (2012) 'Does filtering preclude us from
> >>> studying ERP time-courses?'
> >>>
> >>> I'm not a filter expert. Would you help me Clemens?
> >>>
> >>> Makoto
> >>>
> >>>
> >>>
> >>> 2012/8/9 Guillaume Lio <guillaume.lio at isc.cnrs.fr>:
> >>>>
> >>>>
> >>>>
> >>>> 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
> >>>>
> >>>> Causal filters always cause phase distortions.
> >>>> FIR filters cause linear phase distortions.
> >>>> IIR filters cause non-linear phase distortions.
> >>>>
> >>>> The filtfilt procedure makes filters not causal to produce zero-phase
> >>>> filters.
> >>>> But, the matlab built-in filtfilt fonction works only with FIR
> filters.
> >>>>
> >>>> If you want to do the same procedure with IIR filters, you have to use
> >>>> the filtfiltHD function.
> >>>> filtfiltHD function can be found here :
> >>>> http://www.mathworks.com/matlabcentral/fileexchange/17061-filtfilthd
> >>>>
> >>>> Hope this help.
> >>>>
> >>>> Guillaume Lio
> >>>>
> >>>>
> >>>>
> >>>>
> >>>>> 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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> >>>
> >>>
> >>> --
> >>> Makoto Miyakoshi
> >>> JSPS Postdoctral Fellow for Research Abroad
> >>> Swartz Center for Computational Neuroscience
> >>> Institute for Neural Computation, University of California San Diego
> >>> _______________________________________________
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> >>
> >
> >
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