[Eeglablist] RE : line noise removal issues

[Andre Achim]

I once observed that fluorescent lighting tends to inject odd harmonics of
the line noise in the EEG. Fluorescent tubes even in an adjacent room could
well be responsible for these artefacts. I think that dimmers on
incandescent lights are also susceptible to produce artefacts. Lights
functioning on DC are safer in an EEG lab.
 
I am sending this in case it may help.
 
André Achim

-----Message d'origine-----
De : eeglablist-bounces at sccn.ucsd.edu
[mailto:eeglablist-bounces at sccn.ucsd.edu] De la part de David Contreras Ros
Envoyé : 10 janvier 2008 09:54
À : Tim Mullen
Cc : eeglablist at sccn.ucsd.edu
Objet : Re: [Eeglablist] line noise removal issues


Try SOBI instead of ICA infomax. It seems to isolate line noise better (at
least from my limited experience). SOBI is also implemented in EEGLAB.

Regards, 

David.

Tim Mullen wrote: 

Dear EEGLAB users,

I have a question regarding the use of ICA for line noise removal.

I have some electrocorticographic (ECoG) data with a strong 60Hz line noise
artifact as well as a 180Hz harmonic (only odd harmonics seem to be present,
probably due to symmetrical clipping). 

I am applying frequency-domain granger causality to this data, but have run
into some serious problems with the presence of this line noise. Oddly
enough, the line noise dominates as a directional effect in the granger
causality (unless there is an apparent temporal delay between channels at 60
Hz, a peak at 60 Hz should only be present in the instantaneous causality).
This is likely because the phase at 60Hz appears to differ between channels.
The strength of the directional effect at 60 and 180Hz is so strong that it
dominates any other interesting nearby features, making it impossible to
analyze causal interactions within a wide range of frequencies of interest. 

The noise band is far too wide for notch filtering to be considered a
suitable solution.  I have then tried extended infomax ICA (as implemented
in EEGLAB's runica function), to isolate the subgaussian noise components. I
have attempted this both in automatically estimating the number of
sub-gaussian sources and also fixing the number of subgaussian sources to 1,
2, etc. None of these approaches have been successful.  ICA appears to
converge properly and the covariance matrix of the estimated components is
the identity matrix (it's at least second-order independent). 

It is possible that the tanh function used to model the subgaussian source
distributions is unsuitable for this line noise source. Has anyone used or
implemented any other families of distributions to calculate the score
function for ICA?

Des anyone have any recommendations on how to remove this line noise, either
via source separation or other techniques?  In particular, if anyone has
developed a plugin for EEGLAB or their own code for automatic line noise
removal, that would be optimal. 


Thanks much for your input!

Regards,
Tim 


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