[Eeglablist] triangular / sawtooth / zig-zag pattern in spectral data

Bachman, Peter bachman at psych.ucla.edu
Mon May 12 12:03:47 PDT 2014


Thanks, Matt!  That would also explain why my results look fairly normal, after epoching (and demeaning), and other offline processing.  I appreciate the suggestion.

Thanks,
Pete
________________________________________
From: Matt Craddock [matt.craddock at uni-leipzig.de]
Sent: Monday, May 12, 2014 11:31 AM
To: Bachman, Peter; eeglablist at sccn.ucsd.edu
Subject: Re: [Eeglablist] triangular / sawtooth / zig-zag pattern in spectral data

On 12/05/2014 18:53, Bachman, Peter wrote:
> Hi everyone,
>
>
> When I plot spectral data from continuous EEG, the resulting
> Power-by-Frequency graph looks very strange.  Specifically, starting at
> about 5 Hz and continuing on to higher frequencies, the plots for each
> channel have a repeating triangular, or sawtooth-like, or zig-zag shape.
>
>
> Here is a Dropbox link to an image of representative data:
> https://www.dropbox.com/s/lddxgzlti9m20vj/FFT_triangle_pattern.JPG
>
>
> It looks like some kind of wacky filter has been applied, but the only
> filter in the processing pipeline is a very conventional 0.16-100 Hz
> bandpass.
>
>
> The problem isn't a feature of the raw data itself, because opening the
> same continuous data in different EEG analysis programs and applying a
> FFT produces very normal looking spectra.  This happens across different
> versions of EEGLAB, Matlab, and Windows.
>
>
> The continuous data were recorded using a Biosemi system and saved in
> .bdf format.
>
>
> Has anyone run into this before?  I’d be very grateful for any suggestions.
>
>
> Thanks!
>
> Pete
>
>

Hi Pete,

This is typical with Biosemi. Here's an old post from Bradley Voytek
about this very issue:

http://sccn.ucsd.edu/pipermail/eeglablist/2008/002229.html

It's probably some sort of rounding error somewhere down the line. He
suggests removing the mean from each channel during import. I tried that
and it didn't solve the issue; I think sometimes demeaning based on the
full continuous data might not work if there's an abrupt shift in offset
somewhere in the data - the abrupt shift means the data either side of
the shift isn't centred on zero as it should be after subtraction of the
mean. Anyway, if you're planning on epoching the data, baseline
correction will also solve the problem, in my experience.

Cheers,
Matt

--
Dr. Matt Craddock
Research Fellow
Institute of Psychological Sciences
University of Leeds



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