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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.<br>
<br>
Regards, <br>
<br>
David.<br>
<br>
Tim Mullen wrote:
<blockquote
cite="mid:1e7cfd2c0712101556l6c7f3cb3ycb46746458609b2c@mail.gmail.com"
type="cite">Dear EEGLAB users,<br>
<br>
I have a question regarding the use of ICA for line noise removal.<br>
<br>
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). <br>
<br>
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 <i>directional </i>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.
<br>
<br>
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 <i>runica</i> 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).
<br>
<br>
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?<br>
<br>
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. <br>
<span style="font-family: Times; font-size: 16px;"></span><br
clear="all">
<br>
Thanks much for your input!<br>
<br>
Regards,<br>
Tim
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