[Eeglablist] epoch baseline removal experience?

Arnaud Delorme arno at ucsd.edu
Tue Jun 12 02:12:37 PDT 2012 

Dear Matthew,

removing the mean from each channel will not change anything (in fact ICA performs mean removal from each channel automatically).

Yes, you can prune your dataset aggressively, compute ICA and apply the weights to a version of the dataset that has been less aggressive pruned. This is especially relevant if you want to use ICA to study brain sources (not artifacts) and you do not have a large number of data epochs. Applying ICA to the aggressively pruned dataset allows you to obtain a "good" decomposition where ICA is not overwhelmed by artifacts and applying this decomposition to the less aggressive pruned dataset allows you to process enough data epochs. Of course, both datasets (aggressively pruned and less aggressively pruned) must come from the same session and same subject.

Hope this helps,

Arno

On May 28, 2012, at 8:53 PM, Matthew Stief wrote:

> Hi Guillaume,
> 
> Thanks for these comments.  Why specifically do you think that 2 Hz
> would make looking at P1 not viable?  Is it simply that it will tend
> to shift its latency?  It seems unlikely that it will attenuate it's
> amplitude all that much, unlike larger later components with more low
> frequency content. If all it's doing is shifting the latency, then if
> I am only interested in computing the mean amplitude within some
> latency window, then why can't I simply shift the latency window I'm
> computing a bit?  I had seen Stefan's suggestion, but am not entirely
> sure how to implement it.  One question in particular that I had was
> how to apply ICA weights derived from epoched data from which bad
> epochs had been pruned onto the original unfiltered data that had not
> been pruned in that way.  Is this unproblematic? Do the two datasets
> have to "match" in every way other than that one of is filtered and
> the other is not?
> 
> I also noticed that elsewhere Arnaud had expressed an objection to
> this plan.  In this e-mail he suggested that the presence of offsets
> in the unfiltered data will be problematic for computing component
> activity. Has this concern been addressed, and if so how? Perhaps the
> strategy of removing the mean of each channel prior to filtering would
> ameliorate this concern?
> 
> http://sccn.ucsd.edu/pipermail/eeglablist/2012/004634.html
> 
> Thanks again!
> 
> -Matthew
> 
> 
> On Sun, May 13, 2012 at 5:12 AM, Guillaume Rousselet
> <Guillaume.Rousselet at psy.gla.ac.uk> wrote:
>> 
>> Hey,
>> 
>> high-pass filtering at 1 Hz will indeed improve the quality of your ICA. However, it will severely distort your ERPs:
>> 
>> http://www.frontiersin.org/Perception_Science/10.3389/fpsyg.2012.00131/full
>> 
>> High-pass filtering at 2 Hz to look at P1 is really not a viable option, unless you're only interested in P1's latency (assuming you use a non-causal filter) and plan to throw away everything else.
>> 
>> An excellent strategy has been suggested recently by Stephan Debener. It consists in deriving ICs from high-pass filtered data and then applying the weights on unfiltered data:
>> 
>> http://sccn.ucsd.edu/pipermail/eeglablist/2011/004417.html
>> 
>> Best wishes,
>> 
>> Guillaume
>> 
>> On 13 May 2012, at 05:00, Matthew Stief wrote:
>> 
>> 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
>>>>>>> 
>>>>>>> 
>>>>>>> 
>>>>>>> 
>>>>>>> 
>>>>>>> 
>>>>>>> _______________________________________________
>>>>>>> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
>>>>>>> To unsubscribe, send an empty email to
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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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>> 
>> 
>> 
>> ************************************************************************************
>> Guillaume A. Rousselet, Ph.D., senior lecturer & deputy post-graduate convenor
>> 
>> Centre for Cognitive Neuroimaging (CCNi)
>> Institute of Neuroscience and Psychology
>> College of Medical, Veterinary and Life Sciences
>> University of Glasgow
>> 58 Hillhead Street
>> G12 8QB
>> 
>> http://www.psy.gla.ac.uk/staff/index.php?id=GAR01
>> 
>> Email: Guillaume.Rousselet at glasgow.ac.uk
>> Fax. +44 (0)141 330 4606
>> Tel. +44 (0)141 330 6652
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>> 
>> The University of Glasgow, charity number SC004401
>> ************************************************************************************
>> 
> 
> 
> 
> --
> _________________________________________________________________
> 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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