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figS1a_simCCEPTotalChs.m
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figS1a_simCCEPTotalChs.m
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%% This script is derived from simCCEPLoop, except an outer loop varies the number of total channels
% We simulate 30 reps for each combination of total channels and responsiveness and test CARLA's accuracy
%
% 2024/02/12
%
% If this code is used in a publication, please cite the manuscript:
% "CARLA: Adjusted common average referencing for cortico-cortical evoked potential data"
% by H Huang, G Ojeda Valencia, NM Gregg, GM Osman, MN Montoya,
% GA Worrell, KJ Miller, and D Hermes.
%
% CARLA manuscript package.
% Copyright (C) 2023 Harvey Huang
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program. If not, see <https://www.gnu.org/licenses/>.
%
%% Control parameters of simulated data
srate = 4800;
tt = -0.5:1/srate:1-1/srate;
ntrs = 12; % number of trials
reps = 30; % number of reps for each condition
cmSens = [1, 165/255, 0]; % use orange as color for the sensitive optimum
Aglobal = 0;
% Total number of channels to simulate for each condition
nChsToTest = [25, 20, 15, 10];
%% Outer loop through number of total channels
rng(nChsToTest(1)); % Set seed depending on what number of channels we want, for reproducibility
for nn = 1:length(nChsToTest)
nchs = nChsToTest(nn);
fprintf('Number of channels = %d\n', nchs);
%% Inner loop through number of responsive channels
for nresp = 0:0.2*nchs:0.8*nchs % 0 to 80% responsiveness, by 20% intervals
%% Create data
fprintf('%d of %d channels responsive\n', nresp, nchs);
outdir = fullfile('output', 'simLoopNChs', sprintf('nchs%d-%d', nchs, nresp));
mkdir(outdir);
chsResp = 1:nresp; % first x channels responsive, for ease. order doesn't matter. will sort and color them red
%%
for rr = 1:reps % multiple repetitions at each significance
fprintf('.');
% i) artifact only
V0 = zeros(length(tt), nchs);
Aart = 50 + rand(nchs, 1)*5; % slightly different artifact amplitudes for each channel
artifact = sin(2*pi*600*tt)';
artifact(tt < 0 | tt > 0.002) = 0;
V0 = V0 + artifact*Aart';
% A) Add the evoked potentials
A = 100;
V1 = V0;
sig = genRandSig(tt, length(chsResp), A);
V1(:, chsResp) = V0(:, chsResp) + sig;
% B) Option to add a global noise
if Aglobal
sigCommon = genRandSig(tt, 1, Aglobal);
else
sigCommon = 0;
end
V2 = V1 + sigCommon;
% C) Add common noise to all channels at each trial
V3 = repmat(V2', 1, 1, ntrs); % ch x time points x trial
phLN = rand(ntrs, 3)*2*pi; % LN phases
LN = zeros(length(tt), ntrs);
for ii = 1:ntrs
LN(:, ii) = 8*sin(2*pi*60*tt - phLN(ii, 1)) + 2*sin(2*pi*120*tt - phLN(ii, 2)) + 1*sin(2*pi*180*tt - phLN(ii, 3));
end
% brown noise shared across channels
BN = cumsum(0.4*randn(2*length(tt), ntrs)); % variable brown noise coefficient now
BN = ieeg_highpass(BN, srate, true);
BN = BN((0.5*length(tt)+1) : 1.5*length(tt), :);
noiseCommon = LN + BN;
V3 = V3 + shiftdim(noiseCommon, -1);
% D) add random brown noise
noiseRand = cumsum(0.4*randn(nchs, 2*length(tt), ntrs), 2); % give double the number of time points so we can highpass it
for ii = 1:nchs
noiseRand(ii, :, :) = ieeg_highpass(squeeze(noiseRand(ii, :, :)), srate, true);
end
noiseRand = noiseRand(:, (0.5*length(tt)+1) : 1.5*length(tt), :);
V4 = V3 + noiseRand;
%% Apply CARLA and plot outputs
[Vout, CAR, stats] = CARLA(tt, V4, srate, true); % get the sensitive output
% number of channels used for the CAR
nCAR = length(stats.chsUsed);
[~, nCARglob] = max(mean(stats.zMinMean, 2)); % number of channels at global maximum
% Plot average zmin across trials
figure('Position', [200, 200, 400, 300], 'Visible', 'off'); hold on
errorbar(mean(stats.zMinMean, 2), std(stats.zMinMean, 0, 2), 'k-o');
plot(nCARglob, mean(stats.zMinMean(nCARglob, :), 2), 'b*'); % global max as blue
if nCARglob ~= nCAR; plot(nCAR, mean(stats.zMinMean(nCAR, :), 2), '*', 'Color', cmSens); end
yline(0, 'Color', 'k');
saveas(gcf, fullfile(outdir, sprintf('zmin_rep%d', rr)), 'png');
saveas(gcf, fullfile(outdir, sprintf('zmin_rep%d', rr)), 'svg');
% Sort and plot channels by increasing covariance, draw line at cutoff
V4MeanSorted = mean(V4(stats.order, :, :), 3);
respBool = antifind(chsResp, nchs);
respBool = respBool(stats.order); % logical array of where responsive channels are
cm = zeros(nchs, 3);
cm(respBool, 1) = 1; % make red
figure('Position', [200, 200, 250, 600], 'Visible', 'off');
yspace = 80;
ys = ieeg_plotTrials(tt, V4MeanSorted', yspace, [], cm, 'LineWidth', 1);
yline(ys(nCARglob)-yspace/2, 'Color', 'b', 'LineWidth', 1.5);
if nCARglob ~= nCAR; yline(ys(nCAR)-yspace/2, 'Color', cmSens, 'LineWidth', 1.5); end
xlim([-0.1, 0.5]); set(gca, 'xtick', [0, 0.5]);
xlabel('Time (s)'); ylabel('Channels');
saveas(gcf, fullfile(outdir, sprintf('chsSorted_rep%d', rr)), 'png');
saveas(gcf, fullfile(outdir, sprintf('chsSorted_rep%d', rr)), 'svg');
close all;
% Accuracy values. positive means responsive/excluded from CAR
% We keep these variables as named here, but note that FN and FP are now renamed RCM and NCM in the manuscript.
TP = sum(find(respBool) > nCAR); % responsive channels successfully excluded from CAR (above the cutoff)
TN = sum(find(~respBool) <= nCAR); % NR channels successfully below or at cutoff
FN = sum(find(respBool) <= nCAR); % responsive channels incorrectly included in CAR. *This matters most
FP = sum(find(~respBool) > nCAR); % NR channels incorrectly excluded from CAR
% same for the global threshold
TPglob = sum(find(respBool) > nCARglob);
TNglob = sum(find(~respBool) <= nCARglob);
FNglob = sum(find(respBool) <= nCARglob);
FPglob = sum(find(~respBool) > nCARglob);
fid = fopen(fullfile(outdir, sprintf('accuracy_rep%d.txt', rr)), 'w');
fprintf(fid, 'TP\t%d\nTN\t%d\nFN\t%d\nFP\t%d\n', TP, TN, FN, FP);
fprintf(fid, 'TPglob\t%d\nTNglob\t%d\nFNglob\t%d\nFPglob\t%d', TPglob, TNglob, FNglob, FPglob);
fclose(fid);
end
fprintf('\n');
end
end