[Eeglablist] significance of negative power in an ERSP

Brian Murphy brian.murphy at unitn.it
Wed Dec 15 02:26:54 PST 2010


Hi Giulia,

negative power in ERSP is always relative to the baseline that you used. 
So it does not mean negative power per se - just that the power is lower 
in that band than it was before your event of interest.

Basic references for understanding the general meaning of spectral power 
are:
Event-related EEG/MEG synchronization and desynchronization: basic 
principles
da Pfurtscheller, FH Lopes da Silva - Clinical Neurophysiology, 1999 - 
Elsevier
Mining event-related brain dynamics
da S Makeig, S Debener, J Onton… - Trends in Cognitive Sciences, 2004 - 
Elsevier
The cognitive correlates of human brain oscillations
da MJ Kahana - Journal of Neuroscience, 2006 - neuro.cjb.net
Human gamma-frequency oscillations associated with attention and memory
da O Jensen, J Kaiser… - TRENDS in Neurosciences, 2007 - Elsevier

best,

Brian

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>
> Subject:
> [Eeglablist] significance of negative power in an ERSP
> From:
> Giulia Righi <grighi at gmail.com>
> Date:
> Mon, 13 Dec 2010 22:10:43 +0100
> To:
> "eeglablist at sccn.ucsd.edu" <eeglablist at sccn.ucsd.edu>
>
> To:
> "eeglablist at sccn.ucsd.edu" <eeglablist at sccn.ucsd.edu>
>
>
>
>
>
>
> HI all
>
>
>
> I am sorry to bother the list with such a theoretical question.
>
> However I was wondering if anyone has a good reference to think about the significance of negative power as measured in an ERSP.
>
> What neural processes can it be related to?
>
>
>
> thank you
>
> giulia
>
>
>
>
>
> <><><><><><><><><><><><><><><> 
>
> Giulia Righi, PhD
>
> Postdoctoral Research Fellow
>
> Laboratories of Cognitive Neuroscience
>
> Division of Developmental Medicine
>
> Children’s Hospital Boston/Harvard Medical School
>
> 1 Autumn St.
>
> Boston, MA 02215-5365
>
> Ph: (857) 218-5211 | Fax: (617) 730-0518
>
> ------------------------------------------------------------------------
>
> Subject:
> Re: [Eeglablist] How to correctly break down AR runica() in case of 
> huge sets.
> From:
> Jason Palmer <japalmer29 at gmail.com>
> Date:
> Mon, 13 Dec 2010 22:36:40 +0100
> To:
> 'Mahesh Casiraghi' <mahesh.casiraghi at gmail.com>, 
> "eeglablist at sccn.ucsd.edu" <eeglablist at sccn.ucsd.edu>
>
> To:
> 'Mahesh Casiraghi' <mahesh.casiraghi at gmail.com>, 
> "eeglablist at sccn.ucsd.edu" <eeglablist at sccn.ucsd.edu>
>
>
>
>
> Hi Mahesh,
>
> Merging the results by simple averaging probably won’t work since the 
> components are returned in random order (even after the variance 
> sorting, components won’t necessarily have the same index.) Using 
> matcorr() or a similar component matching algorithm before averaging 
> is one possibility.
>
> But it seems to me that averaging will not improve anything in your 
> situation. As long as you have enough data in each data block that ICA 
> runs on, then the components you get should be well determined, 
> allowing you to remove the artifacts separately, and use the separate 
> unmixing matrices to decompose the different subsets.
>
> I’m not sure what kind of analysis you’re doing, but for many 
> purposes, you want to identify brain components of interest and then 
> analyze the activations and possibly localize them. In this case you 
> only need to match up the components of interest in the separate 
> decompositions, e.g. a frontal midline ERN component, and collect all 
> the trials with the activations produced by the respective ICA 
> unmixing matrices.
>
> Again, as long as you use as much data as you can load (possibly 
> overlapping data blocks), the decompositions should be good by 
> themselves. Comparing the components of interest across decompositions 
> will give you an idea of how stable the components you’re looking at 
> really are in your dataset. You might also look into characterizing 
> the variance of the component maps in a bootstrapping sense, using a 
> large number of resampled blocks.
>
> It would also be possible to modify the ICA algorithm to swap out data 
> from the disk, but as I said, I doubt using all the data would improve 
> the results over using as much data as you can load into memory. To me 
> it makes more sense to verify the stability of the components you’re 
> interested in, and use the separate ICA unmixing/sphere matrices on 
> their corresponding data blocks, and separately back-project the 
> components of interest, and then collect all the trials for the final 
> analysis.
>
> Hope this is useful.
>
> Best,
>
> Jason
>
> *From:* eeglablist-bounces at sccn.ucsd.edu 
> [mailto:eeglablist-bounces at sccn.ucsd.edu] *On Behalf Of *Mahesh 
> Casiraghi *Sent:* Saturday, December 11, 2010 6:34 PM *To:* 
> eeglablist at sccn.ucsd.edu *Subject:* [Eeglablist] How to correctly 
> break down AR runica() in case of huge sets.
>
> Dear more experienced EEGLabbers and ICA experts,
>
> supposing one has to work with quite large datsets (several channels, 
> very high sample rate, long record lengths) and would therefore be 
> unable to load in memory several gigs of data altogether:
>
> A) Is it methodologically problematic to run independent ICAs on 
> subgroups of trials and then separately perform AR (blinks and scalp 
> detected ECG components rejection) on each of them?
>
> B) Assuming it would not be, as I tend indeed to think, a so 
> recommendable way, is there a methodologically proof way to combine 
> all the obtained - and presumably heterogeneous - sphere, weights and 
> weights(-1) matrices in 3 single Sph, W, and W(-1) matrices and then 
> use these new to backproject after component rejection?
>
> C) More precisely, let's suppose we have 700 trials and we run 7 
> independent ICAs each time on 100 of them.
>
> a) I would proceed in picking-up separately (subjective criteria, 
> adjust, faster or whatever one may prefer) the to-be-rejected 
> components, independently from each subgroup of trials.
>
> b) I would then remove subgroup by subgroup the respective w(-1) 
> columns and EEG.icaact rows according to the discarded components.
>
> c) I would merge the obtained 7 EEG.icasphere, the 7 EEG.icaweights, 
> and the 7 EEG.icawinv, in 3 single matrices of equal dimensions, 
> averaging through nanmean (given the fact we are likely to pick up a 
> different amount of components from each of the trial subgroups and we 
> would need consistent matrix dimensions).
>
> d) I would finally independently backproject subgroup by subgroup 
> using the same averaged EEG.icawinv and EEG.icasphere and each time 
> the EEG.icaact of the current subgroup of trials.
>
> According to my first speculations, following a->b->c->d we should 
> come up with something analogous to the output of a big global ICA.
>
> Am I wrong?
>
> D) Did someone among you already try to run something like that and is 
> perhaps willing to provide some feedbacks-impressions?
>
> Cheers,
>
> Mahesh
>
> Mahesh M. Casiraghi
>
> PhD candidate - Cognitive Sciences
>
> Roberto Dell'Acqua Lab, University of Padova
>
> Pierre Jolicoeur Lab, Univesité de Montréal
>
> mahesh.casiraghi at umontreal.ca <mailto:mahesh.casiraghi at umontreal.ca>
>
> I have the conviction that when Physiology will be far enough 
> advanced, the poet, the philosopher, and the physiologist will all 
> understand each other.
>
> Claude Bernard
>
>
> ------------------------------------------------------------------------
>
> Subject:
> [Eeglablist] asking help for 64 channel GSN-Hydrocel's complete 10-20 
> equivalents
> From:
> gump forrest <naturalgump at gmail.com>
> Date:
> Mon, 13 Dec 2010 13:51:13 +0100
> To:
> "eeglablist at sccn.ucsd.edu" <eeglablist at sccn.ucsd.edu>
>
> To:
> "eeglablist at sccn.ucsd.edu" <eeglablist at sccn.ucsd.edu>
>
>
>
> Hi, I am looking for a list of the complete 10-20 correspondence for 
> EGI's 64-channel net (GSN-HydroCel-65 1.0). I've read the technote of 
> the sensor layouts, but it is a bit fuzzy. Does anyone would help me? 
> Thanks in advance. Best, gump
>
>
>
>   


-- 
Brian Murphy
Post-Doctoral Researcher
Language, Interaction and Computation Lab
Centre for Mind/Brain Sciences
University of Trento
http://clic.cimec.unitn.it/brian/



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