[Eeglablist] Artifact removal validation

[Scott Makeig]

Borna - You might be interested in some discussion of artifact
identification in this recent article.

Scott Makeig
Neuroimage. <javascript:AL_get(this, 'jour', 'Neuroimage.');> 2007 Feb
15;34(4):1443-9. Epub 2006 Dec 26.

*Enhanced detection of artifacts in EEG data using higher-order statistics
and independent component analysis.*

*Delorme A*<http://www.ncbi.nlm.nih.gov/sites/entrez?Db=PubMed&Cmd=Search&Term=%22Delorme%20A%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVCitation>,
*Sejnowski T*<http://www.ncbi.nlm.nih.gov/sites/entrez?Db=PubMed&Cmd=Search&Term=%22Sejnowski%20T%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVCitation>,
*Makeig S*<http://www.ncbi.nlm.nih.gov/sites/entrez?Db=PubMed&Cmd=Search&Term=%22Makeig%20S%22%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVCitation>
.

Computational Neurobiology Laboratory, Salk Institute for Biological
Studies, 10010 N. Torrey Pines Road, La Jolla, CA 92107, USA. arno at salk.edu

Detecting artifacts produced in EEG data by muscle activity, eye blinks and
electrical noise is a common and important problem in EEG research. It is
now widely accepted that independent component analysis (ICA) may be a
useful tool for isolating artifacts and/or cortical processes from
electroencephalographic (EEG) data. We present results of simulations
demonstrating that ICA decomposition, here tested using three popular ICA
algorithms, Infomax, SOBI, and FastICA, can allow more sensitive automated
detection of small non-brain artifacts than applying the same detection
methods directly to the scalp channel data. We tested the upper bound
performance of five methods for detecting various types of artifacts by
separately optimizing and then applying them to artifact-free EEG data into
which we had added simulated artifacts of several types, ranging in size
from thirty times smaller (-50 dB) to the size of the EEG data themselves (0
dB). Of the methods tested, those involving spectral thresholding were most
sensitive. Except for muscle artifact detection where we found no gain of
using ICA, all methods proved more sensitive when applied to the
ICA-decomposed data than applied to the raw scalp data: the mean performance
for ICA was higher and situated at about two standard deviations away from
the performance distribution obtained on raw data. We note that ICA
decomposition also allows simple subtraction of artifacts accounted for by
single independent components, and/or separate and direct examination of the
decomposed non-artifact processes themselves.

On 6/19/07, Borna Noureddin <bornan at ece.ubc.ca> wrote:
>
> Hello,
>
> I have been working through the most recent tutorial on artifact
> rejection, and have a question about validation.  How can I validate or
> evaluate the performance of ICA for artifact removal?
>
> The tutorial gives some steps for identifying artifact components, but
> they are quite subjective.  This is further compounded by the fact that,
> given the same set of data, running ICA gives a different set of
> components each time.  So, even with an acceptable validation method,
> it's unclear how I can objectively and reliably apply that method.
>
> Is there, for example, a "post-artifact-removal" version of the tutorial
> dataset that can be compared with the original tutorial dataset provided
> with EEGLAB?  And if so, is there documentation about how precisely the
> artifacts were removed?
>
> Thanks,
> Borna Noureddin
>
>
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-- 
Scott Makeig, Director and Research Scientist, Swartz Center for
Computational Neuroscience, Institute for Neural Computation, University of
California San Diego, La Jolla CA 92093-0961, http://sccn.ucsd.edu/~scott
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