[Eeglablist] Filter distortion around boundary events

Wed Jul 18 12:04:00 PDT 2018

Dear Andreas,

Just a quick input,

> Not sure how Cleanline copes with the TMS artifacts, however.

Good point. CleanLine assumes that the noise (in this case, specifically
50/60Hz line noise) to be stationary across time (and purely sinusoidal).
Temporally sparse noise like TMS artifact cannot be removed by CleanLine.

Makoto

On Tue, Jul 17, 2018 at 10:39 PM Andreas Widmann <widmann at uni-leipzig.de>
wrote:

> Hi Brian,
>
> this is indeed a prototypical filter edge artifact. Most commonly this
> type of artifact is observed with notch filters (but only because notch
> filters are most commonly applied for this type of interference) but can
> also be observed with low-pass or high-pass filters.
>
> I'm afraid that you will not get completely rid of this problem. Your line
> noise interference is very strong (~1 mV peak-to-peak max on AF3 and ~500
> µV peak-to-peak on average across channels). Filters need some time to
> achieve full attenuation. The problem can, however, be significantly
> reduced using somewhat different filter settings.
>
> First, I would suggest to separate high-pass and low-pass filter. You may
> then use lower low-pass filter orders considerably reducing the temporal
> extent of the artifact. Further, I would suggest to use a lower low-pass
> cutoff frequency, e.g. 45 Hz (i.e., passband edge 40 Hz).
> EEG = pop_eegfiltnew(EEG, 1,[],6600,0,[],1);
> EEG = pop_eegfiltnew(EEG, [],40,660,0,[],1);
>
> When filtering, the data have to be padded at the edges. We do pad with a
> DC constant to reliably avoid DC artifacts. With a harmonic oscillation in
> the data, padding with a constant implies amplitude modulation and
> amplitude modulation spectrally smears the line noise interference to
> adjacent bands at the edges of the signal. Therefore you have to use cutoff
> frequencies further away from the line noise interference frequency if the
> signal edges are important and cannot be cut away. Intuitively, mirror
> padding might might be an apparent solution to this problem. However, this
> will introduce other nice edge artifacts due to phase reset and DC shifts.
>
> Usually, I would recommend to try other ways to remove the line noise
> interference, for example Cleanline or DFT filters. Not sure how Cleanline
> copes with the TMS artifacts, however. One more comment: The line noise
> interference is really strong, even for a recording in an unshielded
> environment. Might there possibly be a problem with common mode rejection
> in your EEG setup?
>
> Hope this helps! Best,
> Andreas
>
> > Am 16.07.2018 um 20:48 schrieb Erickson <ericksonb.eng at gmail.com>:
> >
> > Thanks for your responses.
> >
> > Andreas, I used the "basic FIR filter" on automatic order calculation
> filtering from 1 to 55.
> >
> > Here is a drive link to a zip file with 30s of example data. These data
> were imported using fileIO and the event channel (65) was imported as
> events (Nothing else has been done to this example data).
> > https://drive.google.com/open?id=1KPlpeBCrCnQJHCJOcMYsnNSJVOjj0bTO
> >
> > Then I plotted the data and cut out one section around a TMS event, and
> another section NOT around a TMS event;
> > EEG = eeg_eegrej( EEG, [4615 5372;11482 12002]);
> > [ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, 1,'setname','with
> Rejections','gui','off');
> >
> > filtered the data using the FIR filter from 1 to 55hz;
> > EEG = pop_eegfiltnew(EEG, 1,55,6600,0,[],1);
> > [ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, 2,'setname','with
> Rejections and Filter','gui','off');
> >
> > then plotting the data, the artifact is visible as a "pinching" around
> each boundary, with a faster oscillation riding on top. So basically just
> reject any section of data and filter and the artifact appears.
> >
> > Makoto, to answer the question about the spectra - if I cut out the TMS
> pulses and then filter, the PSD looks pretty normal. Prior to filtering
> there is so much DC power I can't visually inspect the data very well.
> >
> > The room is very noisy, but this artifact appears only wherever I make a
> boundary and filter so it seems like an edge effect due to filtering - but
> I've never encountered this kind of artifact before since EEGLAB knows not
> to filter over boundaries. Perhaps I cannot use the automatic filter order
> here due to some noise profile in my data?
> >
> > Thanks to you both for considering the problem!
> >
> > Brian
> >
> >
> > On Thu, Jul 12, 2018 at 8:54 PM, Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
> wrote:
> > Dear Erickson
> >
> > Is this really a distortion introduced by the filter? Rather, isn't it
> the case that that portion of the data was poorly filtered i.e., your
> original data, before filtering, had that noise constantly? Could you
> please check your original data by eyeballing the raw time series and by
> checking power spectral density?
> >
> > Makoto
> >
> > On Thu, Jul 12, 2018 at 10:33 AM Erickson <ericksonb.eng at gmail.com>
> wrote:
> > Hello EEGLAB list,
> >
> > We are experiencing a strange artifact on only some of our EEG data. For
> some subjects, when we cut out portions of data (creating boundary events)
> and then filter (from 1 to 40 or 50hz) we observe a distortion around the
> boundary.
> >
> > Specifically, we see a rising or falling wave on either side of the
> boundary, with a fast oscillation riding on top of it. See here:
> https://drive.google.com/open?id=1yt6h_WLgI6jWAApVd52nwVLuiduYgXIH
> >
> > Although this data is from concurrent TMS-EEG recording, the distortion
> has nothing to do with the TMS pulse itself - we get the same
> boundary-related distortion when we filter after cutting out a random
> section of continuous data (a section with no TMS in it).
> >
> > I am at a loss - we've tried a few different filters and nothing seems
> to solve this issue. Meanwhile, on some subjects there is no
> boundary-related filter distortion at all! There is a lot of noise in the
> room but I'm not sure how that could create this specific issue.
> >
> > Any perspectives on this issue are appreciated! Thank you,
> >
> > Brian
> >
> > Brian Erickson, Ph.D.
> > Postdoctoral Researcher, CogNeW Lab
> >
> > Drexel University
> > 3141 Chestnut Street
> > Stratton Hall Room 320
> > Philadelphia, PA 19104
> >
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> >
> > --
> > Makoto Miyakoshi
> > Swartz Center for Computational Neuroscience
> > Institute for Neural Computation, University of California San Diego
> >
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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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