[Eeglablist] Scalp maps vs. scripted ERSP

Pollet S.C. sebastien.pollet at soton.ac.uk
Mon Feb 6 03:14:48 PST 2017


Hello,

I have 20 epoched datasets from 10 different subjects - 2 datasets per subject as there were 2 different conditions. I created averaged scalp maps (for all 10 subjects together, for all channels) in the alpha range (8-12Hz) using the 'Study/Precompute channel measures' and 'Study/Plot channel measures' functions. I used 10 EEGLAB '.set' files for one condition and another 10 for a different condition.

When I ran the 'Precompute channel measures routine', I used these parameters: 'cycles', [3 0.5], 'baseline', [-3500 -1500], 'freqs', [2 60], 'alpha', 0.05, 'ntimesout', 400, 'padratio',1.

I then used 'Plot channel measures', using '8 12' (8-12Hz) as the frequency parameter and did this repeatedly for 100ms time windows to see what is happening from -1000ms before the event to 1500ms after the event.

I then wanted to plot ERSP over time for a select number of channels (as visually identified using the scalp maps - those with strong ERD/ERS ), so I used a function created in MatLab that uses the 'newtimef' function, which creates the 'ersp_sig' variable containing significant ERSP. 'ersp_sig' is a 3-element vector - ersp_sig(a,b,c) where 'a' is frequency,'b' is time points (400 per epoch), and 'c' is channels (35 in this case).

Here is the function I used:
-------------------------------------------------------------------------------

function [ersp,ersp_sig] = plotERSPTopo(dirname,filename)

eegchannels = 1:35;
cycles = [3 0.5];
freqs = [2 60];
baseline = [-3500 -1500];
tlimits = [-4000 4000];
alpha = 0.05;
mine = -7;
maxe = 7;

eeg = pop_loadset(filename,dirname);

close all;
for i=eegchannels

    tmpsig = eeg.data(i,:,:);
    tmpsig = tmpsig(:);
    triallength = size(eeg.data,2);
    close all;
    fprintf('PROCESSING CHANNEL #%2.0f\n' ,i);
    [ersp(:,:,i),itc,powbase,times,freqs,erspboot,itcboot] = ...
        newtimef(tmpsig,triallength,tlimits,eeg.srate,cycles, ...
        'baseline',baseline, 'alpha', alpha, 'freqs', freqs,'padratio',1, ...
        'plotersp','on','plotitc','off','timesout',400, ...,
        'title',eeg.chanlocs(i).labels,'erspmax',7);

    erspimage{i} = getframe(gcf,[116 80 347 308]);


    for i1=1:size(ersp,1);
        for i2=1:size(ersp,2);
            if (ersp(i1,i2,i)>erspboot(i1,1)) & (ersp(i1,i2,i)<erspboot(i1,2));
                ersp_sig(i1,i2,i)=0;
            else
                ersp_sig(i1,i2,i)=ersp(i1,i2,i);
            end
        end
    end

end

-------------------------------------------------------------------------------
I then used the following function to plot what I was interested in.
X is the file with all 10 datasets appended together for one condition and Y is another file with 10 appended datasets for the other condition.
(These are the same datasets I used to create the scalp maps).
-------------------------------------------------------------------------------

function erd_alpha_AB10 = erd_alpha_AB10(X,Y)

% Plot, for AB10, ERD% for selected channels

%Load file for left movements
ersp_alpha_left_sig = load(X,'ersp_sig');
ersp_alpha_left_sig = ersp_alpha_left_sig.ersp_sig;

%Load file for right movements
ersp_alpha_right_sig = load(Y,'ersp_sig');
ersp_alpha_right_sig = ersp_alpha_right_sig.ersp_sig;

%Convert to ERD/ERS%
erd_alpha_left_sig=((10.^((ersp_alpha_left_sig)/10))-1)*100;
erd_alpha_right_sig=((10.^((ersp_alpha_right_sig)/10))-1)*100;

%Calculate mean values for the alpha range
%(alpha range (8-12Hz) is actually indices 7-11 as frequency range starts at 2Hz)

for j=1:400;
    for k=1:35;
            erd_alpha_left_sig_mean(j,k)= (erd_alpha_left_sig(7,j,k)+erd_alpha_left_sig(8,j,k)+erd_alpha_left_sig(9,j,k)+erd_alpha_left_sig(10,j,k)+erd_alpha_left_sig(11,j,k))/5;
    end
end

for j=1:400;
    for k=1:35;
            erd_alpha_right_sig_mean(j,k)= (erd_alpha_right_sig(7,j,k)+erd_alpha_right_sig(8,j,k)+erd_alpha_right_sig(9,j,k)+erd_alpha_right_sig(10,j,k)+erd_alpha_right_sig(11,j,k))/5;
    end
end

% Plots
t=[-4000:20:3999];

% Left
ch1_left=12; %FC2
ch2_left=27; %CP4
ch3_left=23; %CP3
figure;
title(['Alpha ERD% for all AB10 subjects for left sided movements']);
xlabel('Time, in ms (relative to movement onset at 0)');
ylabel('ERD/ERS %')
axis([-4000 4000 -100 100]);
hold on;
plot(t,erd_alpha_left_sig_mean(:,ch1_left));
plot(t,erd_alpha_left_sig_mean(:,ch2_left),'r');
plot(t,erd_alpha_left_sig_mean(:,ch3_left),'g');
legend('FC2','CP4','CP3','Location','NorthWest');

% Right
ch1_right=11; %FCz
ch2_right=23; %CP3
ch3_right=27; %CP4
figure;
title(['Alpha ERD% for all AB10 subjects for right sided movements']);
xlabel('Time, in ms (relative to movement onset at 0)');
ylabel('ERD/ERS %')
axis([-4000 4000 -100 100]);
hold on;
plot(t,erd_alpha_right_sig_mean(:,ch1_right));
plot(t,erd_alpha_right_sig_mean(:,ch2_right),'r');
plot(t,erd_alpha_right_sig_mean(:,ch3_right),'g');
legend('FCz','CP3','CP4','Location','NorthWest');

end

-------------------------------------------------------------------------------
When I look at the plots, they don't quite match what I see on the scalp maps. For example when I plot FC2, CP3 and CP4 together, my plots show that CP4 is activated before FC2 and a with a greater value, which is not what I see on the scalp maps - FC2 has a dark blue 'patch' (strong ERD) whilst CP4 is green - so I was expecting to see this reflected on the plot.


I don't know where I've got it wrong. Is there an obvious error I have made? I wonder if the way EEGLAB calculates things when producing scalp maps is different from the script I have, and if you might know of a way that I could check this? Can I access the data that is used to create the scalp maps?


Thanks for any advice on this.

Seb

Sebastien Pollet
University of Southampton
sebastien.pollet at soton.ac.uk<mailto:sebastien.pollet at soton.ac.uk>

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