ICA based artifact correction and phase-locking

Markus Werkle-Bergner werkle at MPIB-BERLIN.MPG.DE
Fri Feb 23 13:39:31 CET 2007

Hi Christian,

thank you for your fast reply. Indeed, I had the same thought and tried
it out at least for the power-measures (the analysis for the PLI is
currently underway). The results look somewhat similar (i.e., I used the
original data from one subject (artifacts rejected according to the
semi-automatic procedure) and compared the results with the ICA-cleaned
data), although there are some changes: From my current view, I see the
same spots of increased TF-power (in gamma but also in lower frequency
ranges) in both analyses. However the main spots appear somewhat
deminished in the ICA-cleaned data. Additionally, for high gamma-power
(> 55 Hz), I recognize a power increase in the main spots.

Because I expected some changes (i.e., I assumed to get rid of some
artifactual muscle activity),  now I can not judge whether these results
are 'good' or 'bad'. Therefore, I would be interested in the 'slightly
longer answer'.

Furthermore, currently I do the ICA decomposition on all available
segments (minus those with really excessive artifacts, which I took out
before running ICA). This means, my trials are a mixture of clearly
contaminated as well as clearly uncontaminated trials. Because my main
goal is to identify the eye and muscle artifacts, would it improve the
detection of components reflecting artifacts if I do the ICA
decomposition only on pre-identified artifactual trials? (My idea is
that then the components reflecting artifacts would catch less 'true'
brain activity. But perhaps I have a missconception here ...)

Thank you very much for your help.

Best regards,

Christian Hesse schrieb:
> Hi Markus,
> the short answer is: if (and only if, big if by the way) ICA correctly
> separates artifacts from brain activity, and you correctly identify
> the artifact components, removal of the artifact components from your
> data does not affect the time-frequency properties (including phase)
> of the other components (i.e. the rest of the data) if you project
> back to the sensor level. This is because ICA assumes a linear
> (instantaneous) mixing of statistically independent signals.
> I would just try it out for the  time being - if you get funny results
> (or things too good to be true), please get back in touch as there is
> also a slightly longer answer.
> Hope this helps,
> Christian
> On 23 Feb 2007, at 12:24, Markus Werkle-Bergner wrote:
>> Dear all,
>> in my studies, I'm investigating early preceptual binding (visual)
>> across the lifespan (i.e., I have data form children, younger and older
>> adults) with EEG measures. My main interest concerns changes in
>> gamma-power and measures of phase-synchronization in the gamma
>> frequency range(e.g., phase-locking index, n:m (theta:gamma) phase
>> synchronization).
>> Currently  I use a 'semi-automatic' procedure for artifact rejection,
>> i.e., I use thresholding in the time-domain (min/max in segment -/+
>> 100┬ÁV)to 'suggest' contaminated epochs. After that I visually inspect
>> the data again for eye-blink and muscle activity, and completely
>> reject the contaminated epochs.
>> The problem with this procedure is that, especially in the older
>> adults group, for many subjects only too few trials remain in the
>> final sample.
>> Therefore, I thought I could use ICA for artifact correction (instead
>> of complete rejection). After identification of the components that
>> reflect muscle activity (and also other artifacts), I thought to
>> recombine the remaining ICs and perform my analyses (power, PLI, n:m
>> synchronization) on the recombined (cleaned data).
>> Now my question(s): Is there any experience whether removing certain ICs
>> may change the phase spectrum, i.e. may this approach induce some
>> systematic bias? If there is a systematic bias, are different frequency
>> bands affected differentialy? Could anyone give me some references on
>> these issues?
>> Any comments are very much appreciated.
>> Best regards,
>> Markus
>> --
>> **************************************************************
>> Markus Werkle-Bergner, Dipl. Psych.
>> Predoctoral Research Fellow
>> Center for Lifespan Psychology
>> Max Planck Institute for Human Development
>> Lentzeallee 94, Room 211, D-14195 Berlin, Germany.
>> Phone: +49(0)30-82406-447       Fax: +49(0)30-8249939
>> **************************************************************
> ----------------------------------------------------------------------
> Christian Hesse, PhD, MIEEE
> F.C. Donders Centre for Cognitive Neuroimaging
> P.O. Box 9101
> NL-6500 HB Nijmegen
> The Netherlands
> Tel.: +31 (0)24 36 68293
> Fax: +31 (0)24 36 10989
> Email: c.hesse at fcdonders.ru.nl <mailto:c.hesse at fcdonders.ru.nl>
> Web: www.fcdonders.ru.nl <http://www.fcdonders.ru.nl>
> ----------------------------------------------------------------------

Markus Werkle-Bergner, Dipl. Psych.
Predoctoral Research Fellow

Center for Lifespan Psychology
Max Planck Institute for Human Development
Lentzeallee 94, Room 211, D-14195 Berlin, Germany.
Phone: +49(0)30-82406-447       Fax: +49(0)30-8249939

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