[Eeglablist] R: Re: Epoching eeg data

Wed Sep 11 15:28:44 PDT 2013

Dear Stefania,
you are correct. By interpolating channels we are decreasing the linear
independency of the channels and this is where the rank-deficiency problem,
discussed several times on the list, emerges. It is better to interpolate
channels after ICA. Actually - EEGlab produces a warning when interpolation
is performed on data without ICA weights.

@Valeri
You are correct, but what Makoto was referring to is that any difference
between two ICA decompositions on identical data - but one in continuous
and other in epoched form - is not due to epoching (but the stochasticity
of ICA).
 11 wrz 2013 21:07, "sf.ang at libero.it" <sf.ang at libero.it> napisał(a):

> Hi all,
>
> I am wondering regarding the order of preprocessing.. is it really ok to
> interpolate bad channels before running ICA? Shouldn't we first run ICA
> only on the good channels and later interpolate bad ones which were
> excluded from the ICA?
>
> Thanks
>
> Stefania Ficarella
>
>
>
>
>  ----Messaggio originale----
> Da: mmiyakoshi at ucsd.edu
> Data: 11/09/2013 3.06
> A: "Katherine Naish"<K.R.Naish at pgr.reading.ac.uk>
> Cc: "eeglablist at sccn.ucsd.edu"<eeglablist at sccn.ucsd.edu>
> Ogg: Re: [Eeglablist] Epoching eeg data
>
> Dear Katherine,
>
> > The previous messages largely answer my query, but I'd like just to make
> sure I understand correctly. The results of ICA decomposition should be
> similar whether the data is epoched or not- is that correct?
>
> Correct. If you have the same number of datapoint before and after
> epoching, ICA results should be as identical as the two results from
> running ICA twice on the same data.
>
> > Obviously the results will be different if the data itself is different,
> but does ICA essentially treat epoched data as a continuous section?
>
> ICA shuffles all datapoints as a preprocess; thus the concept of time
> disappears.
>
> > My pre-processing order is the following: resample, filter, epoch,
> remove baseline, interpolate bad channels and remove bad epochs (based on
> visual inspection), re-reference to average, and (finally) run ICA to
> detect artefacts.
>
> It looks ok.
>
> > One thing I wasn't sure about was re-referencing- am i doing that at the
> right point?
>
> Yes.
>
> Makoto
>
> 2013/9/10 Katherine Naish <K.R.Naish at pgr.reading.ac.uk>
>
>>  Dear all,
>>
>>
>>
>> The previous messages largely answer my query, but I'd like just to make
>> sure I understand correctly. The results of ICA decomposition should be
>> similar whether the data is epoched or not- is that correct? Obviously the
>> results will be different if the data itself is different, but does ICA
>> essentially treat epoched data as a continuous section?
>>
>>
>> I would also really appreciate it if someone could approve, or otherwise,
>> my pre-processing order. My pre-processing order is the following:
>> resample, filter, epoch, remove baseline, interpolate bad channels and
>> remove bad epochs (based on visual inspection), re-reference to average,
>> and (finally) run ICA to detect artefacts. One thing I wasn't sure about
>> was re-referencing- am i doing that at the right point?
>>
>>
>> Many thanks in advance,
>>
>> katherine
>>
>>
>>
>>       ------------------------------
>> *From:* eeglablist-bounces at sccn.ucsd.edu [
>> eeglablist-bounces at sccn.ucsd.edu] on behalf of Mikołaj Magnuski [
>> imponderabilion at gmail.com]
>> *Sent:* 06 June 2013 19:13
>> *Cc:* eeglablist at sccn.ucsd.edu
>> *Subject:* Re: [Eeglablist] Epoching eeg data
>>
>>  Dear Karlo,
>>
>>  there is a case when dividing your whole 15 min. into smaller epochs
>> (say 1 or 2 seconds long)
>> is quite convenient with respect to ICA:
>> --> cleaning data before ICA is easy if you just have to click bad epochs
>> instead of marking
>> bad periods in continuous data - although this is just my personal
>> feeling.
>> Nevertheless, you can also use automatic artifact rejection methods
>> available in EEGlab this way. These methods require epoched data.
>> (it is also easy to reconstruct your rejections later if you do them
>> on epochs - you can just save the indices of windows that you removed
>> [by
>> coping relevant field in EEG structure after marking bad epochs,
>> but before rejecting them]).
>> --> Then, after your first IC decomposition, if you are not satisfied
>> with its results and observe that
>> some parts of your data may be responsible for poor results - you can
>> remove
>> epochs containing these data easily and perform ICA again.
>>
>>  So, just as Makoto wrote, ICA does not care whether data are epochedbut you may -
>> because in some cases it is convenient in preprocessing or
>> 'postprocessing' (correcting
>>  ICA decomposition).
>> If you need any extra help with this - let me know (I have some scripts
>> and functions to do this).
>>
>>  Mikolaj
>>
>>
>> W dniu środa, 5 czerwca 2013 użytkownik Makoto Miyakoshi napisał:
>>
>>> Dear Karlo,
>>>
>>>  If you want to epoch your data into 3-min blocks, for example, put
>>> event markers every 3 minutes then epoch the data.
>>>
>>>  ICA does not care temporal continuity of the data since it shuffles
>>> them up differently for every iteration as a preprocess. That means, if the
>>> total number of datapoints are the same before and after the epoching, the
>>> ICA results should be the same.
>>>
>>>  Makoto
>>>
>>>
>>> 2013/6/3 karlo gonzales <thats_karlo at yahoo.com<https://dbxprd0111.outlook.com/owa/UrlBlockedError.aspx>
>>> >
>>>
>>>>  Dear EEGlab experts,
>>>>
>>>>  I need you help regarding an eeg data files which was recorded during
>>>> eye closed state (15 min long).  Thus, there is no event to epoch the data.
>>>> 1) How do you epoch such data?  2) should we epoch data before running
>>>> ica (dose more epoch means better results?)
>>>>  Thanks in advance
>>>>
>>>> _______________________________________________
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>>>>
>>>
>>>
>>>
>>>  --
>>> Makoto Miyakoshi
>>> Swartz Center for Computational Neuroscience
>>> Institute for Neural Computation, University of California San Diego
>>>
>>
>>
>> --
>> Pozdrawiam,
>> Mikołaj Magnuski
>>
>> _______________________________________________
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>
>
>
> --
> Makoto Miyakoshi
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
>
>
>
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