[Eeglablist] epoch baseline removal experience?

Wed May 16 00:10:02 PDT 2012

Thank you Jason for your answer,

Explanation under 4) is exactly what I needed, since I did the filtering,
re-referencing, epoching (+baseline removal) of the data and after that I
wanted to perform ICA in order to remove detected artifacts, after which I
should do the ERP analysis.

The thing is that I have a lot of epochs overlapping, so I am thinking on
switching of the steps "epoching" and ICA in way to

- first perform ICA on (on filtered, re-referenced) data
- after that I can do the epoching (+baseline removal) and ERP analysis

I will also try making non-overlapping epochs and performing ICA on them.

All the best,

Ida

On Wed, May 16, 2012 at 12:11 AM, Jason Palmer <japalmer29 at gmail.com> wrote:

> Hi Yuan-fang,****
>
> Some notes in response to your questions:****
>
> **1)      **Removing major artifacts, such a large spikes in the data,
> before filtering can be preferable since filtering can “spread” the
> artifact out over “good” data, requiring more data to be rejected after
> filtering. When you remove major artifacts, and “boundary” event replaces
> the removed data. Filtering is only applied to continuous data segments,
> not across boundaries.****
>
> **2)      **The main considerations regarding epoched vs. continuous data
> are: a) amount of data given to ICA, and b) stationarity of data given to
> ICA. ICA expects the data to be stationary, i.e. the same statistical model
> is generating all time points. If you have enough data after epoching, then
> epoched data may be preferable since it will increase stationary as you
> mention.  However, you may want to epoch on different events to produce
> different datasets, with the same ICA decomposition for all conditions.
> More data generally gives a better ICA decomposition, assuming all the data
> is similar statistically.****
>
> **3)      **So the idea is to run ICA on the continuous, cleaned,
> filtered data, then epoch. Filtering will removed slow drifts in the data,
> and make the channels zero mean. There is obviously no baseline until you
> epoch the data. It is possible if you only care about one condition and
> want to run ICA on epoched data to remove the baseline first before running
> ICA. Results may differ slightly (particularly when using multiple ICA
> models) so you may want to compare results on your data. In my view,
> removing the baseline before running ICA on epochs is not “bad practice”,
> and shouldn’t affect the estimation of e.g. theta and alpha components, or
> higher frequency components. The main issue is whether the original
> (high-pass) filtering works and produces epochs with “flat” zero mean
> baseline periods, or whether the filtering does not work sufficiently well,
> and your epoch baselines are not always zero mean. You might also try
> “detrending” the epochs before ICA if they are particularly recalcitrant.*
> ***
>
> **4)      **Longer epochs are preferable because they yield more data for
> ICA (assuming stationarity holds.) However, if you are epoching before ICA,
> you don’t want to give ICA overlapping epochs, since it will then have
> duplicates of some data skewing the statistical model. So the epoch start
> time should not be before the stop time of the previous epoch, and the stop
> time should not be after the start time of the next epoch.****
>
> **5)      **Dipolarity, i.e. residual variance (R.V.) of dipole fit, of a
> component map is one indication of component quality. If the R.V. is low,
> then the component is physiologically localized and concisely accounted for
> potentially by a single patch of cortex. A good decomposition should have
> many dipolar components. Mutual information can be used to assess how
> independent a component is from other components. And the “Quality” of
> spectral peaks of independent component activation is also informative.
> E.g. theta and alpha components should have stronger theta or alpha peaks
> than the raw channel data, and should account for most of the theta or
> alpha activity in the data in one or a few components. ERP averages should
> also be “cleaner” and “simpler” in the independent components than in the
> raw channel data.****
>
> Best,****
>
> Jason****
>
> ** **
>
> ** **
>
> ** **
>
> *From:* eeglablist-bounces at sccn.ucsd.edu [mailto:
> eeglablist-bounces at sccn.ucsd.edu] *On Behalf Of *Yuan-fang Chou
> *Sent:* Sunday, May 13, 2012 6:41 AM
> *To:* Matthew Stief
> *Cc:* smakeig at ucsd.edu; eeglablist at sccn.ucsd.edu; mmiyakoshi at ucsd.edu
> *Subject:* Re: [Eeglablist] epoch baseline removal experience?****
>
> ** **
>
> Dear Makeig,****
>
> ** **
>
> I still feel quite confused about your post.Please forgive me for my
> ignorance as a newbie in ICA.****
>
> My questions can be outlined as follows:****
>
> 1)Why should we first remove major artifacts and then filter the data?Can
> we invert these two procedures?****
>
> 2)ICA should be conducted in epoched data or continuous data?If both are
> ok,which is better?For continuous data often contains large amounts of
> artifacts,which happens during the interval of each trial in experiment,I
> think it may be better to do ICA on epoched data.****
>
> 3)Why should baseline removal be done after ICA?I really don't understand
> the reason under this practice.****
>
> 4)Why longer epochs are more enjoyable for ICA?****
>
> 5)Are there some indicators which can  used to identify if the result of
> ICA are good enough to make inference?****
>
> Sorry for the long question list and wish for your reply.****
>
> ** **
>
> 2012/5/13 Matthew Stief <ms2272 at cornell.edu>****
>
> Hi Scott,
>
> Thanks for this. If you're going to baseline-zero epochs after ICA, then
> what's the point of baselining the whole dataset before epoching? Just to
> have an additional kind of high pass filter?  You're saying that doing this
> AND a ~1Hz high-pass filter would be better for the ICA than just doing the
> high-pass filter, right? I thought that the advantage of doing the
> whole-epoch baseline (and thus also i assume this whole dataset baseline
> removal), was that it ameliorated problems of low frequency drift for the
> ICA without suffering from the attenuation of large later components caused
> by an aggressive high pass filter. So I was thinking of it as an
> alternative to high pass filtering, not an addition to it. In my current
> data processing strategy I've gone for not baseline removing before ICA at
> all, and just relying on an aggressive 2 Hz high-pass filter (all I care
> about is the P1), and then doing a baseline removal for epochs after the
> ICA. But you're saying doing this big baseline removal and a high pass
> produces superior results, right?
>
> Also, I wasn't sure from your e-mail whether you thought the whole dataset
> baseline removal should occur before or after filtering. I've been doing
> major artifact removal after filtering because it makes bad patches easier
> to see, but i'd be happy to do it this way if it creates a better ICA
> decomposition to do this kind of total baseline removal.
>
> Thank you!
>
> -Matthew****
>
>
>
> ****
>
> On Fri, May 11, 2012 at 11:09 PM, Scott Makeig <smakeig at gmail.com> wrote:*
> ***
>
> Even whole-epoch baseline removal is not ideal.  It is better to
> zero-baseline the data after major artifact-period removal but before
> epoching (and, typically, high-pass filtering above ~1 Hz). Only then
> extract epochs for ICA decomposition (IF you do not want to decompose the
> continuous data -- our more typical procedure). After ICA decomposition,
> data epochs can be individually baseline-zeroed without affecting the ICA
> account of them.****
>
> ** **
>
> Scott****
>
> On Fri, May 11, 2012 at 12:31 PM, Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
> wrote:****
>
> Dear Ida and Scott,
>
> > As I understood, the purpose of Baseline Removal is for me/us to have
> > better insight when event in observed epoch happened, so the value around
> > corresponding marker is expected to be zero. Right?
>
> That sounds right, although I may not understand you perfectly.
> ERP show up usually after the event (unless it is expectation-related
> nature), so it makes sense to set the baseline period before stimulus
> onset during which brain activity is supposed to be neutral, and
> whatever ERP can be compared against it.
>
> > I have one more question regarding this - does it matter if I Remove
> > Baseline for example (-1000ms to 0ms) if I have epoch that is longer (-4
> > secs to 4 secs)? I read in Q&A list Arno's answer regarding similar
> question
> > where he said that ICA can be unstable if the epochs baseline is too
> short,
> > so he suggests longer baselines (i.e 1 sec).
>
> Although I don't know what Arno meant in that specific context, I
> guess he was probably referring to the finding reported by Groppe,
> Makeig, and Kutas (2009). In the paper, the authors reports
> whole-epoch baseline produced better ICA results compared to short
> pre-stimulus baseline. Therefore, for ICA purpose, it's even better to
> use an entire epoch for a baseline. The authors says 'It is not clear
> what causes this difference.' in the paper (pp.1208), though I heard
> Scott say a brief explanation. What do you think, Scott?
>
> Makoto
>
>
>
> 2012/5/10 ida miokovic <ida.miokovic at gmail.com>:
> > Dear Makoto,
> >
> > thank you for your answer, it cleared the doubts in my head regarding
> this
> > =). As I understood, the purpose of Baseline Removal is for me/us to have
> > better insight when event in observed epoch happened, so the value around
> > corresponding marker is expected to be zero. Right?
> >
> > I have one more question regarding this - does it matter if I Remove
> > Baseline for example (-1000ms to 0ms) if I have epoch that is longer (-4
> > secs to 4 secs)? I read in Q&A list Arno's answer regarding similar
> question
> > where he said that ICA can be unstable if the epochs baseline is too
> short,
> > so he suggests longer baselines (i.e 1 sec).
> >
> > Thanks,
> >
> > Ida
> >
> >
> > On Thu, May 10, 2012 at 9:45 PM, Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
> > wrote:
> >>
> >> Dear Ida,
> >>
> >> The consequence would be that you may not have near-zero potential
> >> at/around time zero (and this time zero which should be an onset of
> >> whatever event). Usually people want to reset their data to zero
> >> microvolt at/around time zero, so they subtract mean of short time
> >> period immediately before it (for example, -200 ms to 0 ms as a
> >> baseline period). Am I answering to your question? If not, let me
> >> know.
> >>
> >> Makoto
> >>
> >> 2012/5/10 ida miokovic <ida.miokovic at gmail.com>:****
>
> >> > Hello everyone,
> >> >
> >> > Since I do not have experience in eeg signal processing, I am asking
> you
> >> > for
> >> > the opinion regarding epoch baseline removal (a window for this pops
> up
> >> > after I do the data epoching). Epochs I am extracting are quite long:
> -4
> >> > secs before and 4 secs after Marker of my interest.
> >> >
> >> > Why is following suggested in tutorial:
> >> >
> >> > "Using the mean value in the pre-stimulus period (the pop_rmbase()
> >> > default)
> >> > is effective for many datasets, if the goal of the analysis is to
> define
> >> > transformations that occur in the data following the time-locking
> >> > events."
> >> >
> >> > What are the consequences if I leave the fields in pop up window
> (Epoch
> >> > Baseline Removal) empty and therefore have the whole epoch used as a
> >> > baseline?
> >> >
> >> > Thank you in advance,
> >> >
> >> > All the best,
> >> >
> >> > Ida
> >> >
> >> >
> >> >
> >> >
> >> >
> >> >****
>
> >> > _______________________________________________
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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
> >
> >
>
>
>
> --
> Makoto Miyakoshi
> JSPS Postdoctral Fellow for Research Abroad
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego****
>
>
>
> ****
>
> ** **
>
> --
> Scott Makeig, Research Scientist and Director, Swartz Center for
> Computational Neuroscience, Institute for Neural Computation; Prof. of
> Neurosciences (Adj.), University of California San Diego, La Jolla CA
> 92093-0559, http://sccn.ucsd.edu/~scott****
>
>
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>
>
> -- ****
>
> _________________________________________________________________
> Matthew Stief
> Human Development | Sex & Gender Lab | Cornell University
> http://www.human.cornell.edu/HD/sexgender
>
>
> Heterosexuality isn't normal, it's just common.
> -Dorothy Parker
>
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> ****
>
> ** **
>
> --
> Yuan-Fang Chao
> School of Psychology
> SouthWest University
> Beibei,Chongqing,China
>
>
> ****
>
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