[Eeglablist] filters, ICA and erp

[Scott Makeig]

I think it's important to realize that ICA looks for the most temporally
distinctive (i.e., independent), spatially stationary (source) activity
patterns. Our strong impression is that EEG activity < 1 Hz may typically
not be well represented as the sum of temporally distinctive, spatially
stationary sources, e.g. with dipolar scalp maps indicating that they can
(and most likely do) represent the projection of synchronous activity within
a single cortical patch.

Such activity may include (or be dominated by) sweat-related changes in skin
conductance that may move across the scalp rather than emanating from a
single cortical patch.

Of course various slow potentials dependent on cognitive expectation etc
 are well known as well, and the Birbaumer group showed that in some sense
we are 'smarter' when our scalp EEG is more negative -- a finding now being
extended by several groups to show (cautiously!) faster learning when
surface-negative fields are applied to the scalp.

So further, more detailed study of ICA (static or extended) decomposition of
slow potentials is likely of value ... but I certainly feel that for most
questions involving ERP peaks the information to be gained from ICA
decomposition of the ~1-Hz high-passed data certainly exceeds the loss of
information from the highpass operation -- and applying the ICA weights to
the wider band data is not a problem -- unless you want to directly
interpret the very slow potentials.

In that case, separating the data into highpass + lowpass portions,
decomposing each by ICA, and then comparing the resulting components (e.g.
their maps, etc) should be of interest.

Scott Makeig



On Sun, Oct 16, 2011 at 4:26 PM, Arnaud Delorme <arno at ucsd.edu> wrote:

> Hi All,
>
> regarding running ICA on the high pass filtered data at 1 Hz, the reason
> this is performed is that ICA tends to focus on the data that express the
> most power (in case of EEG low frequencies). Since very low frequencies can
> represent skin conductance changes that might confuse ICA demixing, it is
> better to remove them. However, it might be interesting to try to run ICA on
> the derivative of the data. Discrete derivation using the diff function is a
> linear operation, and it makes the spectrum flat where the influence of all
> frequencies is almost made equal. ICA on this data would not focus on low
> frequencies. It is an interesting research project that one ought to pursue.
>
> Also, I just wanted to comment on the CORRMAP plugin that Stefan Debener
> and Filipa Viola Campo developed. We have worked on integrating further this
> plugin with EEGLAB STUDY (because it proved very efficient), and we believe
> it is now fully integrated with many new features such as component
> preselection etc... It is distributed by default with EEGLAB.
>
> Thanks,
>
> Arno
>
> On Oct 13, 2011, at 11:59 PM, Stefan Debener wrote:
>
>  Hi Steve,
>
> Yes, ICA training on 1 Hz hp filtered data and applying the weights to the
> unfiltered data was my suggestions (and has been used in Scott's lab for
> many years, I believe). After playing a lot with preprocessing we found this
> to work best as well. The ML code below shows a typical preprocessing
> example implementing this. Note that, due to the HP filter, there is no real
> benefit in baseline correction of the dummy epochs. The rationale for this
> code, as well as a more general introduction into the analysis of
> multi-channel EEG data with ICA (inlcuding other assumptions and relazted
> preprocessing issues), is descriped in our EEG-fMRI book in chapter 3.1.:
>
> Debener et al., (2010). Using ICA for the analysis of mutli-channel EEG
> data. In: Ullsperger & Debener (Eds.). Simultaneous EEG and fMRI: recording,
> analysis, and application (pp. 121-133). Oxford University Press.
>
> Feel free to cite this chapter when you use the code below.
>
> Thanks,
> Stefan
>
> Ps: Sara, on the issue of filtering and ERPs, you should test the effect of
> the filter, by plotting an overlay of the filtered and unfiltered ERPs.
> This, of course, should be done on single subject ERPs, as the grand average
> sometimes acts as a(nother) filter.
>
>
>
> -----------------------------------------------------------------
> % miu_inside_ana03.m
> % sd
> %
> % ica on cnt data
> % ----------------------------------------------------------------
>
> MAINPATH = '/data/sd_miu/pilot_inside/';
> PATHIN = [MAINPATH, 'data/ana02/'];
> PATHOUT = [MAINPATH, 'data/ana03/'];
> SUBJ = {'4102', '4103new', '4105', '4106'};
> HPF = 1;                 % high pass filter for ica training only
> DIM = 63;               % ica dimensionality
> EPOCHDUR = 1;      % epoch duration in seconds for pruning
> THRES = 4;             % SD cut-off for rejection
>
> for s = 1:length(SUBJ)
> [ALLEEG EEG CURRENTSET ALLCOM] = eeglab;
> EEG = pop_loadset('filename', [SUBJ{s}, '_bcgrem.set'], 'filepath',
> PATHIN);
>
> % high pass filter
> EEG = pop_iirfilt(EEG, HPF, 0, [], [0]);
>
> % dummy trigger for epoching
> for i=1:(EEG.srate*EPOCHDUR):EEG.pnts
>     EEG.event(end+1).type = 'S999';
>     EEG.event(end).latency = i;
> end
> EEG = eeg_checkset(EEG);
> %eeg_eventtypes(EEG)
>
> % epoching and pruning
> EEG = pop_epoch(EEG, {'S999'}, [0 EPOCHDUR], 'newname', 'tmp epochs',
> 'epochinfo', 'yes');
> EEG = pop_jointprob(EEG,1,[1:63] ,THRES, THRES,0,0);
> EEG = pop_rejkurt(EEG,1,[1:63] ,THRES, THRES,0,0);
> EEG = eeg_rejsuperpose(EEG, 1, 1, 1, 1, 1, 1, 1, 1);
> EEG = pop_rejepoch(EEG, EEG.reject.rejglobal ,0);
>
> % ICA
> EEG = pop_runica(EEG, 'icatype','runica','dataset',1,'options', {'extended'
> 1, 'pca' DIM },'chanind',[1:63]);
>
> TMP.icawinv = EEG.icawinv;
> TMP.icasphere = EEG.icasphere;
> TMP.icaweights = EEG.icaweights;
> TMP.icachansind = EEG.icachansind;
>
> % apply to cnt dataset
> clear EEG;
> EEG = pop_loadset('filename', [SUBJ{s}, '_bcgrem.set'], 'filepath',
> PATHIN);
> EEG.icawinv = TMP.icawinv;
> EEG.icasphere = TMP.icasphere;
> EEG.icaweights = TMP.icaweights;
> EEG.icachansind = TMP.icachansind;
> clear TMP;
> EEG = pop_saveset(EEG, 'filename',[SUBJ{s}, '_cnt.set'],
> 'filepath',PATHOUT);
> [ALLEEG, EEG, CURRENTSET] = eeg_store(ALLEEG, EEG, 0);
>
> end
> eeglab redraw;
>
> ----------------------------------------
>
>
> Am 10/13/11 6:09 PM, schrieb Steve Luck:
>
> Hi Sara.  Unless you care about frequencies per se, epoching and
> baseline-correcting the data won't be a problem.  From a time-domain
> perspective, this won't change anything.
>
>  BTW, someone else suggested using the 1-Hz high-pass cutoff, performing
> ICA, and then applying the component coefficients to the unfiltered data.
>  That sounds like a great suggestion, although I don't know if there is a
> technical reason why it wouldn't work.  Does anyone out there know if there
> would be a problem with this?
>
>  Steve
>
>  ps- The email trail on this topic has gotten out of hand, so I deleted
> everything except the most recent message and your original message.
>
>  On Oct 13, 2011, at 7:20 AM, Sara Graziadio wrote:
>
>  Steve,
> actually I was refering to your book when I was writing that the filter
> would deforme/reduce the erp. But following David Groppe's suggestion would
> mean to reduce activity at different frequency all across the spectrum,
> wihtout exactly knowing which frequencies I am reducing, am I right? If I
> want to look at the psd as well as at the erps, would this analysis just be
> correct? I am always concerned about applying data modification that I
> cannot fully control..if you know what I mean...
> Thank you very much
> Best
>
> Sara
>
>
>  On Wed, Oct 5, 2011 at 10:46 AM, Sara Graziadio
> <sara.graziadio at newcastle.ac.uk> wrote:
> Hello,
> I would like just a suggestion about some data cleaning/analysis I am
> doing. I
> am doing an ERP analysis and I want to clean my data first with the ICA. In
> theory, though, I should not use an high-pass cutoff higher than 0.1 Hz to
> not
> reduce the erp amplitude. On the other side the ICA does not work well if
> the
> high-pass cutoff is lower than 0.5 Hz...what is then the best method to
> apply?
> Has anybody tested how robust the ica is with a 0.1Hz filter?
> I have also another question: I am doing the analysis on 94 electrodes
> referenced to Fz. I planned to average reference the data but actually
> there is
> quite a large spread of noise on all the electrodes with this method
> (muscular
> artefacts for example from the temporal electrodes). But actually almost
> all
> the papers are using the average reference so I was surprised, am I the
> only
> one having this problem of noise? Would not be better just to keep the Fz
> reference and then perhaps to average the erps for every different cortical
> area and do the analysis on these averaged erps?
>
> Thank you very much
>
> Best wishes
>
> Sara Graziadio
> Research Associate
> Newcastle University
>
>
>       --------------------------------------------------------------------
> Steven J. Luck, Ph.D.
> Director, Center for Mind & Brain
> Professor, Department of Psychology
> University of California, Davis
> Room 109
> 267 Cousteau Place
> Davis, CA 95618
> (530) 297-4424
> E-Mail: sjluck at ucdavis.edu
> Web: http://mindbrain.ucdavis.edu/people/sjluck
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> --------------------------------------------------------------------
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>
> --
> Prof. Dr. Stefan Debener
> Neuropsychology	Lab
> Department of Psychology
> University of Oldenburg
> D-26111 Oldenburg
> Germany
>
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-- 
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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