[FieldTrip] Removing power line

Mon Oct 4 12:43:01 CEST 2021

Hi Emilie,

I agree with Stephen -- absolutely, looking at the power spectrum will help guide your strategy here. It's normally unlikely to see both 50 Hz and 60 Hz in the same place... but I could imagine this may happen if the village gets power from a local electricity grid (likely 50 Hz) but with some buildings using potentially mismatched power inverters on solar panels or diesel generators (can be either 50 Hz or 60 Hz depending on manufacturer). Fluorescent lighting and perhaps some appliances may also generate noise frequencies that are totally unrelated to 50/60 Hz.

Just to clarify, cfg.dftreplace = 'neighbour' uses the spectrum interpolation method that Sabine Leske and I developed. It's actually designed for powerline noise frequencies that are *less* sharp than the default dftfilter method. So while it sounds like you have an extreme situation, it might indeed be an appropriate strategy for your data, depending on what your power spectrum actually looks like.

The design and test scenario was 50 Hz line noise in a typical home environment in Germany, which was actually a precise 50 Hz but of moderately fluctuating amplitude. These sorts of common fluctuations effectively broaden the noise frequency peak from a sharp 50 Hz to, for example, 49-51 Hz. For such a case, you would indeed define cfg.dftbandwidth of 2. The "neighbor" bandwidth is more tricky, but this should represent "clean" frequencies since their amplitude is used to replace the powerline corruption. In this example, a neighbourbandwidth of 1 would imply that the amplitude at 48-49 Hz and 51-52 Hz would be used to "patch" the noise peak at 49-51 Hz.

You may have a more extreme scenario, but nevertheless looking at the power spectrum will help you see how broad each powerline noise peak is (use that to set "dftbandwidth"), and ensure that you pick clean neighbor frequencies (guiding "neighbourbandwidth").

Our paper describing the method has an example of real data and some tips on usage that might be helpful:
https://doi.org/10.1016/j.neuroimage.2019.01.026

Hope this helps!

Cheers,
Sarang

On Sun, 2021-10-03 at 14:45 +0200, Stephen Whitmarsh via fieldtrip wrote:
Dear Emilie,

Those sound indeed like challenging, and interesting, circumstances.
I don't have a direct answer to your question, that would depend on having more information about the data, but here are my 2 cents;

You could first make an FFT plot (e.g. using cfg.method = mtmfft in ft_freqanalysis) to get a better picture of the frequencies in your data (on both 'successful' and 'unsuccessful' data), for several reasons:

  *   You are also trying to remove both 50 and 60Hz, and their harmonics, of which probably only one of those would reflect the noise in the power lines.
  *   You are using cfg.dftreplace = 'neighbour'; which might only work if the frequency band of the noise is sharp enough, or can be estimated precise enough (depending on the duration of the data you are filtering).
  *   It would confirm whether it is line noise at all (which is probable, but more might be at play, e.g. some non-linear combinations and strange harmonics).
  *   You could look for whether the degree of noise is stationary, by e.g. plotting the FFT over different periods, but it might be more important that the frequency is accurately chosen and stable over time.

Depending on your data, the duration of data, whether or not your are looking at trials or the whole data file at once (*), etc., you might also want to use a 'welch method' for estimating the power in the data, i.e. by averaging over several (sliding) windows. Using cfg.method = 'mtmconvol' in ft_freqanalysis, then averaging over time with ft_selectdata (cfg.avgovertime), would be an easy way to do that. Once you have a good picture of the noise, it will be easier to both choose the right filters and to check their efficacy (by comparing the FFT before and after filtering, on both 'successful' and 'unsuccessful' data). In fact, since you are dealing with EEG in such noisy conditions, and assuming you are interested in either ERPs or slow oscillations (<40Hz), you could consider just using a lowpass filter at e.g. 40 Hz.

Good luck, I hope this helps,
Stephen

*) The behaviour of filters depends a lot on the structure/size of the data, so to better understand its behaviour you would need to describe your data(structures) in more detail, i.e. trial length, nr. of trials/channels, and any processing steps until filtering.

Op zo 3 okt. 2021 om 13:12 schreef Emilie Caspar via fieldtrip <fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>>:
Dear Fieldtrippers,

I recently acquired EEG data in Rwanda in complicated testing conditions, especially for the power line : we were testing in rural villages with a single electrical system for the whole village. Our signal is thus parasited by what the villagers were plugging on the electrical system (so probably nonstationary noise) during the testing. The electrical system was also very rudimentary and not correctly grounded.

Usually, I use the following command in Fieldtrip to remove the 50H or 60 Hz power line and it works quite well. Here, it worked for roughly 70% of our acquired data, but it does not work for all the data acquired.

    cfg= [];
    cfg.dftfilter = 'yes';
    cfg.dftfreq = [50 60 100 120 150 180];
    cfg.dftreplace = 'neighbour';
    cfg.dftbandwidth = [2 2 2 2 2 2];
    cfg.dftneighbourwidth = [2 2 2 2 2 2];
    data_intpl = ft_preprocessing(cfg, allData_preprosses);

When I look at the graphs (see figure attached), it really looks like it’s a 60Hz noise, but it seems that the dftfilter function does not remove it. We are certain it’s power noise because when the electricity was cut off because of a storm or else, and we were thus only relying on the batteries to collect our data, the signal was perfect. So in theory we should be able to remove it but we have no cue of what other possibilities to try. Perhaps it’s because the noise is non stationary and dftfilter does not account for that? I know some residual power noise can stay after a dftfiltre, but here it does not remove anything.

Thanks a lot for the help,

Emilie

[cid:17c461876c4c75d62ec1]
---------------------------------------------
Prof. Dr. Emilie Caspar
Associate Professor
Department of Experimental Psychology, Ghent University
office: Henri Dunantlaan, 2 - Floor 2, Room 94
lab’s website: https://moralsocialbrain.com/
personal website: https://emiliecaspar.home.blog/
Université libre de Bruxelles (office & contact): DB10.138 / +32 2 650 32 95

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