[Eeglablist] Channels for ICA

Tue Aug 7 14:02:41 PDT 2018

Thank you very very much for your valuable input! :)

-Fabio

--------------------------------
*Fabio Giatsidis, M.D.*
Resident in Neurology - University of Rome "Tor Vergata" - Rome, Italy
Post-doctoral research fellow - Brown University - Providence, RI, USA

On Wed, Aug 1, 2018 at 2:02 PM, Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
wrote:

> Dear Fabio,
>
> I will disappear from the list until November (others in the lab will
> answer instead), so this is my last response to you on this issue.
>
> > Can I run ICA on the 10-minute continuous recording (even if different
> "states" happen every 2 minutes) so that I have enough data points
> (sqrt(500 Hz x 600 sec / 30) = 100), and *then *do my chunking?
>
> Technically yes.
>
> > Or is this approach invalid because of the variance in the tasks that
> are asked every 2 min?
>
> VERY good point! Such variance difference across task (blocks) is called
> non-stationarity. ICA's assumption is data be stationary. So theoretically
> speaking you are violating (better to say 'undermining') the assumption of
> stationarity. Addressing data non-stationarity is one of the most difficult
> problem in data processing. As far as I know, there is no established ICA
> preprorcessing pipeline there, although the fully-functional code of
> multi-model adaptive-mixture ICA has been there for ten years! We just
> don't know how to integrate the multiple models in a useful way.
>
> In most cases, however, using single-model (i.e., standard) ICA across
> different tasks is fine. My explanation for this is that in EEG occipital
> alpha is always loud (show high amplitude), so is central mu, etc... these
> loud member's behaviors are more or less the same across many types of
> cognitive experiments because they respond to very general cognitive
> function such as attention and moving/not moving etc. You also want to
> remember that ICA is biased to high-amplitude activities.
>
> In practice, if I were you, yes I would concatenate all the blocks to run
> a single ICA, then make comparison on both across the block-separated
> conditions and within-block conditions.
>
> Makoto
>
> On Tue, Jul 31, 2018 at 9:51 PM Giatsidis, Fabio <
> fabio_giatsidis at brown.edu> wrote:
>
>> Thank you Tarik and Makoto for your precious suggestions!
>>
>> Actually, I should have clarified that the 2-minutes recordings are
>> segments from a single 10-minute EEG recording, and the patient was asked
>> to do different things every 2 minutes (keep eyes open, keep eyes closed,
>> etc.). I thought that chunking the continuous recording in small,
>> task-specific sub-recording before start cleaning each sub-recording would
>> have made better sense. But given your answers, a new question comes to my
>> mind:
>> - let's assume I first cut down arbitrarily the number of channels to
>> ~100 or less as per Tarik's suggestion (i.e. excluding those around the
>> ears, etc. that are of little help anyway). Can I run ICA on the 10-minute
>> continuous recording (even if different "states" happen every 2 minutes) so
>> that I have enough data points (sqrt(500 Hz x 600 sec / 30) = 100), and *then
>> *do my chunking? Or is this approach invalid because of the variance in
>> the tasks that are asked every 2 min?
>>
>> Sorry for the confusion, I'm just trying to make the most sense out of
>> what I have and to not make missteps! Thank you immensely for all your help!
>> -Fabio
>>
>> --------------------------------
>> *Fabio Giatsidis, M.D.*
>> Resident in Neurology - University of Rome "Tor Vergata" - Rome, Italy
>> Post-doctoral research fellow - Brown University - Providence, RI, USA
>>
>>
>> On Wed, Jul 25, 2018 at 3:18 PM, Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
>> wrote:
>>
>>> Dear Fabio,
>>>
>>> > I have a (probably naive) question regarding ICA.
>>>
>>> I'll give you my naive answers, not intentionally but due to my
>>> limitations. Follow them at your own risk.
>>>
>>> > - whether I should or not choose the whole 128 channel set for running
>>> ICA
>>>
>>> Yes, unless your data is too short for that. Remember, (number of
>>> channels)^2 x 30 data points at 256 Hz sampling rate is a rule of thumb for
>>> running ICA.
>>>
>>> > - consequently, how I should *a priori* decide which channels to
>>> consider for ICA and which not.
>>>
>>> You can determine a priori which anatomical regions you are going to
>>> analyze. Then, if ICA gives you the '(stationary) effective source
>>> locations' that overlap /are close enough to those pre-selected regions,
>>> pick them up for the final analysis.
>>>
>>> ICA is a hypothesis-free approach, but that does not mean you cannot
>>> have a hypothesis.
>>> You might enjoy reading the classic discussion between ICA pioneers and
>>> Karl Friston about how ICA could be used in neuroscience data analysis.
>>> Friston KJ. Modes or models: a critique on independent component
>>> analysis for fMRI. Trends Cogn Sci.  1998. Oct 01; 2(10) 373-375
>>>
>>> > The recordings are ~2 minutes long and the sampling rate is 1000 Hz.
>>>
>>> If you have only 2 min, you definitely cannot perform >100ch ICA.
>>> sqrt(250 Hz x 120 sec / 30) is about 31, so you want to use 'pca' option
>>> to perform dimension reduction to obtain 31 ICs.
>>>
>>> > I would like to keep as many channels as possible during
>>> pre-processing, and afterwards discard the ones I realize are not useful to
>>> my analysis - if this approach seems reasonable.
>>>
>>> Record longer. 2-min EEG is too short if you want to use ICA.
>>>
>>> Makoto
>>>
>>> On Thu, Jul 19, 2018 at 3:26 AM Giatsidis, Fabio <
>>> fabio_giatsidis at brown.edu> wrote:
>>>
>>>> Hello EEGLAB list,
>>>>
>>>> I have a (probably naive) question regarding ICA.
>>>> I have been using an EGI 128-channel system to record resting states.
>>>> I have been reading a bit about ICA, but it is still not clear to me:
>>>> - whether I should or not choose the whole 128 channel set for running
>>>> ICA, and
>>>> - consequently, how I should *a priori* decide which channels to
>>>> consider for ICA and which not.
>>>>
>>>> The recordings are ~2 minutes long and the sampling rate is 1000 Hz. I
>>>> would like to keep as many channels as possible during pre-processing, and
>>>> afterwards discard the ones I realize are not useful to my analysis - if
>>>> this approach seems reasonable.
>>>>
>>>> Also:
>>>> - if as a very first step I delete some clearly bad channels and then
>>>> interpolate them to repopulate the original channel set, is it legit to
>>>> include such interpolated channels during ICA?
>>>>
>>>> Thank you very much!
>>>> Best,
>>>> -Fabio
>>>>
>>>> --------------------------------
>>>> *Fabio Giatsidis, M.D.*
>>>> Resident in Neurology - University of Rome "Tor Vergata" - Rome, Italy
>>>> Post-doctoral research fellow - Brown University - Providence, RI, USA
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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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