[Eeglablist] Scalp maps vs. scripted ERSP
Makoto Miyakoshi
mmiyakoshi at ucsd.edu
Thu Feb 9 18:34:33 PST 2017
Dear Pollet,
> I don’t know where I’ve got it wrong. Is there an obvious error I have
made?
I can't answer to this question. I also want someone to take a look at my
groupSIFT code to guarantee there is no error.
> 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?
Yes, you can use a debug mode in Matlab. Open one of the functions being
used to generate the figure, put a red circle on the left column of the
edit window, then run the code. The process will stop there, so you can
trace the process line by line. It's a basic technique so if you haven't
tried it yet, I recommend you challenge it this time!
Makoto
On Mon, Feb 6, 2017 at 3:14 AM, Pollet S.C. <sebastien.pollet at soton.ac.uk>
wrote:
> 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
>
>
>
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--
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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