% clear the memory
clear;
% close all open files and windows
close all;
% clear the command console
clc;

% loop through and load the participants
for i = 1:10

    % specify a file name
    fileName = ['Subject ' num2str(i) '.mat'];

    % load the file
    load(fileName);

    % put the data into a variable called fftData
    fftData(:,:,:,i) = FFT.data(:,1:30,:);

end

grandFFT = mean(fftData,4);

fftChannel = 52; % Pz
plot(FFT.frequencies(1:30),grandFFT(fftChannel,:,1));
hold on;
plot(FFT.frequencies(1:30),grandFFT(fftChannel,:,2));

% this will get us the power in the delta range (1 to 3) for both
% conditions for all participants
deltaPower = squeeze(fftData(fftChannel,1:3,:,:));
deltaPower = squeeze(mean(deltaPower,1));

% we will repeat this for theta, alpha, and beta. you will note that the
% only difference is in the frequency range being specified. we are also
% only analyzing one channel
thetaPower = squeeze(fftData(fftChannel,4:7,:,:));
thetaPower = squeeze(mean(thetaPower,1));

alphaPower = squeeze(fftData(fftChannel,8:12,:,:));
alphaPower = squeeze(mean(alphaPower,1));

betaPower = squeeze(fftData(fftChannel,13:30,:,:));
betaPower = squeeze(mean(betaPower,1));

% delta power plot data
deltaPowerPlotData = squeeze(fftData(:,1:3,:,:));
% take the mean across participants
deltaPowerPlotData = mean(deltaPowerPlotData,4);
% and take the mean over 1 to 3 Hz
deltaPowerPlotData = squeeze(mean(deltaPowerPlotData,2));

% we will take the difference between conditions for the headplot, but you
% do not need to
deltaPowerPlotDifference = deltaPowerPlotData(:,1) - deltaPowerPlotData(:,2);

% plot the topo
doPlot2DTopo(deltaPowerPlotDifference,FFT.chanlocs);