[Eeglablist] Large Unexplained Differences in Power Values

Clement Lee cll008 at eng.ucsd.edu
Tue Mar 9 09:47:05 PST 2021


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> 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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