[Eeglablist] Are the results more significant on the scalp or inside brain?

Tue May 20 11:37:29 PDT 2014

Hello,

One way to estimate noise in the data is to use Schimmel's (1967) +/-
reference method which inverts every other trial before averaging. This
cancels out the ERP, leaving the event-unrelated activity behind.

Joe Dien's EP toolkit includes Schimmel's measure as a quality control
feature. Also, the toolkit's documentation clearly explains the
calculations that are involved.

This may or may not be the definition of "noise" you were referring to.
Check the list for previous discussions on estimating the SNR.

Sincerely,

*Allan Campopiano* | MA Candidate
Laboratory of Cognitive and Affective Neuroscience
Brock University | Psychology Department | 500 Glenridge Ave.
St. Catharines, ON Canada L2S 3A1
*T* 905-688-5550 x3451 *F *905-688-6922

On Sat, May 17, 2014 at 12:01 AM, Laxmi Shaw <laxmi.shaw22 at gmail.com> wrote:

> Dear EEGlab list
> Can u help me to find the SNR of the EEG signal.Can anybody send me the
> algorithm step to find the SNR of the EEG signal
>
>
>
> Regards
> Laxmi
>
>
> On Thu, May 15, 2014 at 12:23 AM, Makoto Miyakoshi <mmiyakoshi at ucsd.edu>wrote:
>
>> Dear Cyril,
>>
>> I agree with you. That's why it's always recommended to show confidence
>> intervals or error variances whenever possible.
>>
>> Makoto
>>
>>
>> 2014-05-13 12:44 GMT-07:00 Dr Cyril Pernet <cyril.pernet at ed.ac.uk>:
>>
>>>    Hi Makoto & Michal,
>>>
>>> I agree with Makoto about the ICA subspace which can be quite different
>>> - there is however another thing to consider
>>> You said ' Should the effect be stronger (in terms of more statistically
>>> significant electrodes (dipoles) and timeperiods) on scalp electrodes or in
>>> DIPFIT clusters?'
>>>
>>> the problem here is that statistically significant is an estimate under
>>> H0, so beside the hypothesis test, you cannot tell if the effect is
>>> stronger or weaker in one case or the other because a p value tells nothing
>>> about H1 -- to do that you need to look at the actual effect size (like
>>> what is the mean uV difference between conditions) and not base your
>>> judgment the (correted) p values. You could also test if the effects are
>>> different using a test for apparied measures  (eg. a paired t-test between
>>> (condition A - condition B) on one compoment vs (A -B) on one channel).
>>>
>>> Cyril
>>>
>>> --------------------------------
>>> Dear Michal,
>>>
>>>  That's a simple but deep question.
>>>  Theoretically the difference between condition can't be smaller in ICA
>>> recults since canceling happens in the mixing process and not the other way
>>> around (like the law of entropy?)
>>>
>>>  However, I believe a major problem in comparing channels with ICs is
>>> component selection. The question is how you guarantee that the ICs you
>>> choose is a right representative (projecting source) to the channel? What
>>> if some subject don't have such ICs? What if some subjects have multiple of
>>> such ICs (subspace)?
>>>
>>>  One way to investigate this problem is run pvaf analysis (you have
>>> pvaftopo under EEGLAB plugin manager)
>>>  I have an experience of computing the pvaf analysis across subjects per
>>> cluster (unpublished data), and the result showed very large standard
>>> deviations... it was like mean 30% and SD=30, range 5-80. This means a
>>> cluster can explain a channel activity (in my result, of course) only by
>>> 30%, and there are huge inter-subject variance.
>>>
>>>  This being said, I think it is still ok to stay optimistic and take the
>>> theoretical conclusion. You haven't observed horrendously contradicting
>>> results, have you?
>>>
>>>  Makoto
>>>
>>>
>>> 2014-05-12 14:02 GMT-07:00 Michal Vavrecka <vavrecka at fel.cvut.cz>:
>>> Hello,
>>>
>>> I do have few simple questions and I am curious about your intuitions
>>> and arguments:
>>>
>>> I am finishing the paper where I did group analysis of two cognitive
>>> states. I visualized both scalp maps and dipoles and their statistical
>>> tests. Both visualization are based on fieldtrip monte carlo permutation
>>> with cluster based statistics (correction for multiple comparison). I would
>>> like to interpret the difference between results on the scalp and inside
>>> the brain (DIPFIT). What are your intuitions:
>>>
>>> Should the effect be stronger (in terms of more statistically
>>> significant electrodes (dipoles) and timeperiods) on scalp electrodes or in
>>> DIPFIT clusters?
>>>
>>> How to interpret the stronger effect on the  scalp?
>>> Does the ICA and DIPFIT calculation somehow weaken the ERSP difference?
>>> My intuition is opposite as the source reconstruction has to clean the
>>> noise and strengthen the effect that should result in more statistically
>>> significant timeperiods in the spectrograms compared to scalp data?
>>> Is there any paper that compares these two approaches?
>>>
>>> Thanks for your answers.
>>>
>>> Michal
>>>
>>> --
>>> Dr Cyril Pernet,
>>> Academic Fellow
>>> Brain Research Imaging Center
>>> Neuroimaging Sciences
>>> University of Edinburgh
>>>
>>> Western General Hospital
>>> Division of Clinical Neurosciences
>>> Crewe Road
>>> Edinburgh
>>> EH4 2XU
>>> Scotland, UK
>>>
>>> cyril.pernet at ed.ac.uk
>>> tel: +44(0)1315373661
>>> http://www.sinapse.ac.uk/
>>> http://www.sbirc.ed.ac.uk/cyril
>>>
>>>
>>>
>>> --
>>> The University of Edinburgh is a charitable body, registered in
>>> Scotland, with registration number SC005336.
>>>
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>>
>>
>>
>> --
>> Makoto Miyakoshi
>> Swartz Center for Computational Neuroscience
>> Institute for Neural Computation, University of California San Diego
>>
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>
>
>
> --
> PhD Scholar
> IIT Kharagpur,
> West Bengal
> Ph no-08388837821
>
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