[Eeglablist] Large Unexplained Differences in Power Values

Naomi Heffer nrh31 at bath.ac.uk
Fri Mar 12 01:22:39 PST 2021


Hi Gideon,

Thank you very much for your helpful suggestions.

I am keen to take both of your suggestions on board, but I am not super confident working with Matlab. I wonder if you could provide any more information, or highlight any useful resources, to help me work out how I might extract what was removed by the clean_artifacts command and the best way to go about creating a fabricated dataset?

Many thanks,

Naomi

From: Gideon P Caplovitz <gcaplovitz at unr.edu>
Sent: 10 March 2021 16:52
To: Naomi Heffer <nrh31 at bath.ac.uk>
Subject: Re: [Eeglablist] Large Unexplained Differences in Power Values

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Hi Naomi,
 I am no EEG_LAB expert so take my suggestion with a grain of salt:  perhaps you could extract exactly what was removed by the clean_artifacts command. Power spectrum on the original, cleaned and removed should all line up in an intuitive fashion. If not, then maybe something fishy is going on.
Good luck!
GPC

P.S. I always recommend the fabrication of a *test* dataset, created with hypothetical/anticipated signals and embedded with known noise... all under your control. The dataset can be used to test/debug your entire pipeline start to finish prior to analyzing any real data. Time-consuming yes, but virtually priceless and one of the few ways of being 100% confident in your results.


=======================
Gideon P. Caplovitz
Associate Professor
Director of the Cognitive and Brain Sciences Graduate Program
Department of Psychology
University of Nevada Reno
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=======================




On Mar 10, 2021, at 5:43 AM, Naomi Heffer <nrh31 at bath.ac.uk<mailto:nrh31 at bath.ac.uk>> wrote:

Hi Clement,

Yes, I was comparing 'EEG' and 'clean' in my code.

I used all of the defaults for clean_artifacts except I turned the channel criterion off - thank you for highlighting that this means I will have high-pass filtered the data twice. I have now corrected my code so I only filter the data once but this doesn't seem to make much difference to the absolute power values.

The output I get to the command line when I run clean_artifacts seems to suggest that no channels are removed and that a large amount of the data had to be cleaned, but most was ultimately retained:

Keeping 54.5% (205 seconds) of the data.
eeg_insertbound(): 27 boundary (break) events added.
eeg_insertbound(): 27 boundary (break) events added.
Estimating calibration statistics; this may take a while...
Determining per-component thresholds...done.
Now cleaning data in 13 blocks.............
Now doing final post-cleanup of the output.
Determining time window rejection thresholds...done.
Keeping 96.5% (364 seconds) of the data.
eeg_insertbound(): 10 boundary (break) events added.
eeg_insertbound(): 10 boundary (break) events added.

Looking at the plot from vis_artifacts suggests that the cleaning has removed some very noisy sections with large amplitude changes. Do you think this is enough to be causing the 10^3 difference in magnitude in the obtained power values?

Many thanks,

Naomi

From: Clement Lee <cll008 at eng.ucsd.edu<mailto:cll008 at eng.ucsd.edu>>
Sent: 09 March 2021 17:47
To: Naomi Heffer <nrh31 at bath.ac.uk<mailto:nrh31 at bath.ac.uk>>
Cc: eeglablist at sccn.ucsd.edu<mailto:eeglablist at sccn.ucsd.edu>
Subject: Re: [Eeglablist] Large Unexplained Differences in Power Values

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Hi Naomi,

Are you comparing the power between 'EEG' and 'clean' in your code? What arguments did you use for clean_artifacts (are you using default settings thereby high pass filtering the data twice?) and what does the output in the command line look like (e.g. how many channels or data points removed). Does the vis_artifact step give you any insight on this power difference?

Best,
Clement Lee
Applications Programmer
Swartz Center for Computational Neuroscience
Institute for Neural Computation, UC San Diego
858-822-7535


On Tue, Mar 9, 2021 at 9:13 AM Naomi Heffer <nrh31 at bath.ac.uk<mailto:nrh31 at bath.ac.uk><mailto:nrh31 at bath.ac.uk>> wrote:
Hi there,

I have been trying to calculate power in different frequency bands but I find that whether or not I clean the data using clean_artifacts before running the analysis has a very large impact on the magnitude of the power values.

Here is my code below, along with the absolute power values in each of the frequency bands when (a) I clean the data before running the analysis and (b) I don't use the clean_artifacts function to clean the data. Do you have any idea why I am seeing such massive differences in magnitude? And can you suggest which of the two sets of values is likely to be more realistic?

Many thanks

Naomi

%% 1. Opening & Importing to EEGLAB
       % Get data from .easy to EEGLAB .set/.fdt format

       EEG = pop_easy(filename, 1, 0,[]);

        % Setting the right channel locations. Reading montage file
         EEG = pop_chanedit(EEG, 'load',config,'save','mychans.loc');

%% 2. Filtering

       % Filter data between 0.1 and 40Hz
       % High-pass filter
        EEG = pop_eegfiltnew(EEG, 0.5,[], 1690, 0, [], 0);
      % Low-pass filter
        EEG = pop_eegfiltnew(EEG, [], 35, 86, 0, [], 0);

%% 3.Cleaning of continuous data

      clean = clean_artifacts(EEG, 'ChannelCriterion', 'off');
     vis_artifacts(clean, EEG)


%% 4. Spectral Analysis

[spectra,freqs] = spectopo(EEG.data(:,:,:), 0, EEG.srate, 'freqrange', [1 80], 'plotmean', 'on', 'overlap', 250);

% delta=1-4, theta=4-8, alpha=8-13, beta=13-30, gamma=30-80
deltaIdx = find(freqs>1 & freqs<4);
thetaIdx = find(freqs>4 & freqs<8);
alphaIdx = find(freqs>8 & freqs<13);
betaIdx  = find(freqs>13 & freqs<30);
gammaIdx = find(freqs>30 & freqs<80);

% compute absolute power
deltaPower = mean(10.^(spectra(deltaIdx)/10))
thetaPower = mean(10.^(spectra(thetaIdx)/10))
alphaPower = mean(10.^(spectra(alphaIdx)/10))
betaPower  = mean(10.^(spectra(betaIdx)/10))
gammaPower = mean(10.^(spectra(gammaIdx)/10))



 1.  Values with cleaning:
deltaPower =    3.2846
thetaPower =    2.4440
alphaPower =    8.9593
betaPower =    6.9579
gammaPower =    3.3659


 1.  Values without cleaning:
deltaPower =    1.5560e+03
thetaPower =   1.3541e+03
alphaPower =   2.7626e+03
betaPower =   1.0275e+03
gammaPower =   97.3422

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