[Eeglablist] Too much drift with just 0.1hz high-pass filtering?

[Makoto Miyakoshi]

Thank you Guillaume and Andreas for expert's comments.

> One approach is to inspect and analyze the signal that was removed from
the data by filtering, i.e. the difference between filtered and unfiltered
data.

Oh I see... we apply the same ERP/whatever analysis on the difference wave
to see how much contribution the removed signals have to the hypothesized
effect. That makes a lot of sense.

Makoto

On Fri, Aug 7, 2015 at 10:39 AM, Andreas Widmann <widmann at uni-leipzig.de>
wrote:

> Dear Kevin, Makoto and list,
>
> I fully agree with Makoto’s comments. Further, I would like to emphasize
> that there *cannot* be a *general* recommendation or answer to this
> question (how to filter ERPs). The answer to this question always depends
> on the data, the data quality and signal-to-noise ratio (SNR), the noise
> characteristics, the paradigm, the parameter(s) you want to estimate from
> the data, and other variables.
>
> I would like to argue for two criteria:
> (1) Filters should be applied to improve the SNR (reduce the noise) in
> order to be able to estimate a particular parameter (that is, if there is
> no noise there is no need to filter!).
> (2) Applied filters must not introduce distortions or artifacts
> systematically biasing your estimated parameter.
>
> Two examples:
> * With high frequency noise you might need a lowpass filter to get a valid
> estimate of component peak latency. However, a lowpass filter might also
> lead to a systematic underestimation of component onset latency (due to
> smoothing; VanRullen, 2011). To estimate time window means you usually do
> *not* need a lowpass filter.
> * Many ERP components are not of oscillatory nature but rather introduce a
> strong DC and low frequency component (in particular P3 and other late/slow
> components). Highpass filtering will necessarily spread/smear this activity
> and systematically bias the amplitude of preceding (we use non-causal
> filters!) and subsequent components. In a paradigm with a P3 in one but not
> in another condition (e.g. active oddball) the peak/mean amplitude of the
> preceding N1/N2 might be systematically biased between conditions after
> highpass filtering (e.g. with a higher 1 Hz cutoff while the effects might
> be negligible with a 0.1 Hz cutoff).
>
> My recommendation is to systematically explore the effects a filter has on
> your data. One approach is to inspect and analyze the signal that was
> removed from the data by filtering, i.e. the difference between filtered
> and unfiltered data. Another important approach is to apply filters with
> different parameters and to compare the results (what you already did) and
> the effects on your parameter estimate. You can also filter test signals
> modeling your data and explore the observed filter
> effects/artifacts/distortions.
>
> For your particular question you might want to read the already mentioned
> Acunzo paper and the recent paper by Tanner and colleagues (2015,
> Psychophysiology, DOI: 10.1111/psyp.12437). The point is whether your
> observed „drift" is really noise or evoked activity (I would rather guess
> that it is the latter) and whether removing it by filtering would bias your
> estimated parameter (following your description I would guess yes but this
> is really guessing without seeing the averaged data and knowing the
> dependent variable).
>
> Hope this helps! Best,
> Andreas
>
> > Am 07.08.2015 um 00:58 schrieb Makoto Miyakoshi <mmiyakoshi at ucsd.edu>:
> >
> > Dear Kevin,
> >
> > > But what I'd really like to know is, does *my* 0.1hz data contain
> comparable drift to others' 0.1hz data?
> >
> > Yes, your data look normal for 0.1Hz high-passed.
> > I also recommend you change the time scale so that you can see 10 sec
> long of data in one screen to visually confirm if there is any wave whose
> half cycle (i.e. single peak or trough) goes beyond  5 sec.
> >
> > Widmann et al. (2014), who advocates the merit of filter, wrote:
> >
> > %%%%%%%
> > Some authors argue against high-pass filtering (or restrict the
> applicable high-pass cutoff to frequencies as low as <0.1 Hz; in particular
> if estimating window mean or peak amplitudes; Acunzo et al.,2012; Luck,
> 2005) or low-pass filtering (in
> > particular if estimating onset latencies; VanRullen, 2011). We certainly
> want to stress their point–care is needed–but, on the other hand, if the
> filter applied really increases the signal-to-noise ratio (as it should to
> motivate its usage) and does not systematically bias the to-be-estimated
> parameter, these values can be determined with greater precision with than
> without filtering.
> > %%%%%%%
> >
> > Exaggerated filter phobia is not useful.
> > A common mistake is to think that
> > 'Closer to the original, better the signal'
> > 'Therefore, less use of filter, better the signal'
> > Here, the underlying idea is 'minimizing the use of filter == maximizing
> signal fidelity' But it does not apply to our case; we are not playing
> music where the original source necessarily has the maximum signal
> fidelity. Andreas Widmann's point is 'Hey, what matters is signal-to-noise
> ratio; if filtering can improve it, why not use it rather than avoiding
> it?' This makes sense to me.
> >
> > However, I'm not sure how to measure the signal to noise ratio.
> > In our case, we have been using ICA + mutual information reduction to
> evaluate the goodness of preprocessing, but this is kind of ICA-centric
> view and may not be acceptable for others.
> >
> > Makoto
> >
> > On Thu, Aug 6, 2015 at 12:20 PM, Kevin Tan <kevintan at cmu.edu> wrote:
> > Makoto,
> >
> > Thanks so much for the detailed reply. Indeed, given all the caveats,
> 0.1hz hi-pass produces 'better' ERP waveforms and is in-line with the
> literature.
> >
> > But what I'd really like to know is, does *my* 0.1hz data contain
> comparable drift to others' 0.1hz data? It's hard to tell since people
> don't publish their epoched EEG data. If it is comparable then I can feel
> much better.
> >
> > And yes, for ICA I use 1hz hipass then copy the resulting weights to
> 0.1hz data.
> >
> > Mohammed,
> >
> > I've tried detrend on continuous 0.1hz data with little effect. Do you
> mean detrend on individual epochs? Would that result in consistency issues
> across epochs?
> >
> > Many thanks for the comments!
> >
> > Best,
> > Kevin
> >
> > --
> > Kevin Alastair M. Tan
> > Lab Manager/Research Assistant
> > Department of Psychology & Center for the Neural Basis of Cognition
> > Carnegie Mellon University
> >
> > Baker Hall 434 | kevintan at cmu.edu | tarrlab.org/kevintan
> >
> > On Thu, Aug 6, 2015 at 7:50 AM, Mohammed Jarjees <
> m.jarjees.1 at research.gla.ac.uk> wrote:
> > Dear Kevin Tan,
> > Have you tried detrend function on 0.1 Hz filtered data. It may be help.
> > Best Regards
> > Mohammed Jarjees
> >
> > ________________________________________
> > From: Makoto Miyakoshi [mmiyakoshi at ucsd.edu]
> > Sent: 05 August 2015 06:03
> > To: Kevin Tan
> > Cc: EEGLAB List
> > Subject: Re: [Eeglablist] Too much drift with just 0.1hz high-pass
> filtering?
> >
> > Dear Kevin,
> >
> > In the Appendix of Rousslet (2012), you can find the shockingly bad
> effect of 1-Hz high-pass filter compared with 0.5-Hz or below. However, the
> filter function used here is pop_eegfilt, which is an old generation before
> Andreas Widmann redesign it, so for 500-Hz sampled data the filter order
> for 1-Hz is 75000. After Andreas's fix, it is 1651. 75000 is crazy. You
> need to keep it in mind. FYI, 1-Hz highpass with Hamming window and fiter
> order 75000 results in the transition bandwidth of 0.022; Andreas's
> heuristics suggests 1.
> >
> > > Is the drift in 0.1hz data ok? I get 'better looking' ERP waveforms &
> more robust differences between conditions in 0.1hz data – I'm worried this
> is mostly due to drift.
> >
> > Sadly, it is often the case that our eye are trained for something that
> does not have a good ground. Rousslet (2012) showed 'distortion' of ERP
> waveforms after 1- and 2-Hz highpass (but again with old function).
> However, if you know Gibb's phenomenon etc and the exact filter order you
> are using, you would find nothing wrong. Same goes for your/my impression
> of the 0.1-Hz high-passed data. I would say the waves are drifting and at
> least bad for the purpose of ICA. But for the researchers of EEG infraslow
> oscillations, they would say oh it's a good data.
> >
> > So there is no good or bad. After averaging several hundred trials, the
> apparently drifting signals (to my eyes) will produce 'better' ERP
> waveforms, thanks to the averaging process. If you say you will run ICA on
> the 0.1-Hz highpassed data, I'd say you shouldn't.
> >
> > Stephan Debener's solution is that you apply 1-Hz high-pass on the data,
> run ICA, copy the weight matrix to the 0.1-Hz high-passed data.
> >
> > Makoto
> >
> > On Tue, Aug 4, 2015 at 10:20 PM, Kevin Tan <kevintan at cmu.edu<mailto:
> kevintan at cmu.edu>> wrote:
> > Hi all,
> >
> > There are numerous papers that conclude that >0.1hz high-pass filtering
> distorts ERPs. However, I notice a lot of remaining drift after 0.1hz
> hi-pass, especially compared to 1hz hi-pass. I'm using a BioSemi Active2
> 128ch.
> >
> > 0.1hz hi-pass:
> > https://cmu.box.com/s/1uafw786miveruz85ycj3taxflzg7p7f
> >
> > 1hz hi-pass:
> > https://cmu.box.com/s/t1dbzntjcwdrsp734m949xnzmycvpw5p
> >
> > Is the drift in 0.1hz data ok? I get 'better looking' ERP waveforms &
> more robust differences between conditions in 0.1hz data – I'm worried this
> is mostly due to drift.
> >
> > The 1hz data has ERP 'distortions': negative slope from start of epoch
> until P1 & negative deflection of later components. Thus, I'm not
> comfortable with either of the filters.
> >
> > The screenshots show data run only through 1) PREP pipeline 2) high-pass
> filtering 3) epoching. The final cleaned data shows the same drift.
> >
> > My full preproc stream:
> >
> > ICA dataset:
> > - Load PREP'd data
> > - 1hz hi-pass
> > - Epoch
> > - Epoch rejection
> > - Extended ICA (binica)
> > - Determine bad ICs
> >
> > Final dataset:
> > - Load (unfiltered) PREP'd data
> > - 0.1hz hi-pass (tried 1hz for comparison too)
> > - Epoch
> > - Generate ICs from matrices of ICA dataset
> > - Remove bad ICs determined from ICA dataset
> > - Epoch rejection
> > - DIPFIT
> > - Make ERPs
> >
> > Any input would be much appreciated!
> >
> > Many thanks,
> > Kevin
> > --
> > Kevin Alastair M. Tan
> > Lab Manager/Research Assistant
> > Department of Psychology & Center for the Neural Basis of Cognition
> > Carnegie Mellon University
> >
> > Baker Hall 434<
> https://www.google.com/maps/place/40%C2%B026%2729.5%22N+79%C2%B056%2744.0%22W/@40.4414869,-79.9455701,61m/data=!3m1!1e3!4m2!3m1!1s0x0:0x0>
> | kevintan at cmu.edu<mailto:kevintan at cmu.edu> | tarrlab.org/kevintan<
> http://tarrlabwiki.cnbc.cmu.edu/index.php/KevinTan>
> >
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> >
> >
> > --
> > Makoto Miyakoshi
> > Swartz Center for Computational Neuroscience
> > Institute for Neural Computation, University of California San Diego
> >
> >
> >
> >
> > --
> > 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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