[Eeglablist] line noise removal issues

[Diane Whitmer]

Another method is to use a sliding multi-taper power spectral estimate
with a huge padding factor, estimate the amplitude and phase for each
window, and then subtract off this reconstructed sine wave.
There's free code from www.chronux.org, including an F-statistic to
determine which sinusoidal oscillations are statistically significant.


Here's a relevant references:
Mitra & Pesaran, Biophys J., 1999


-Diane


Doctoral Student
Graduate Program in Computational Neurobiology
Swartz Center for Computational Neuroscience
University of California, San Diego
http://sccn.ucsd.edu

On Wed, 12 Dec 2007, Jeff Eriksen wrote:

> Tim,
>
>
>
> I do not have any code right now, just an idea that I published as an
> abstract many years ago. Basically, it goes like this: Create a 60 Hz
> waveform defined by an amplitude, phase, frequency, and offset. Using some
> kind of non-linear optimization routine (Nelder-Mead downlhii simplex,
> a.k.a. "amoeba" from Numerical Recipes is a start), vary the four parameters
> just mentioned until the objective function is minimized. For an objective
> function, subtract the synthetic 60 Hz waveform from your data, calculate
> the spectrum, and create some metric of the smoothness of the spectrum
> around 60 Hz. This way you do not remove all of the 60 Hz as a notch filter
> would do, but presumably leave in the 60 Hz component of the signal and only
> remove the 60 Hz peak, or additional 60 from the environment.
>
>
>
> Reply to me off this list if you want to discuss further. Obviously you
> would have to make modifications for multiple channels and harmonics.
>
>
>
> -Jeff Eriksen
>
> OHSU Dept BME
>
> Portland, OR
>
>
>
> From: eeglablist-bounces at sccn.ucsd.edu
> [mailto:eeglablist-bounces at sccn.ucsd.edu] On Behalf Of Tim Mullen
> Sent: Monday, December 10, 2007 3:57 PM
> To: eeglablist at sccn.ucsd.edu
> Subject: [Eeglablist] line noise removal issues
>
>
>
> 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
>
>