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figS1b_summarizeSimCCEPTotalChs.m
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figS1b_summarizeSimCCEPTotalChs.m
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%% This script takes outputs from figS1a_simCCEPTotalChs, and summarizes the accuracy for each total channel size and level of responsiveness
% Generates outputs for Figure S1A
%
% 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/>.
%
%% Configure parameters and directories
parCm = brighten(parula(15), -0.5); % black is the 50 ch condition, yellow is lowest number of channels (10)
parCm(1, :) = [0, 0, 0];
parCm(4, :) = [0.4940 0.1840 0.5560];
% Configure the name of the folders to load accuracy scores from.
rootdir = 'simLoopNChs';
rootdir50 = 'simLoopNoGlob'; % We do not regenerate the 50 Chs condition, just pull from previous outputs
nChsToTest = [50, 25, 20, 15, 10]; % total number of channels
fracResp = 0:0.2:0.8; % fraction of all channels responsive
nReps = 30;
%% Read and store accuracy values at each number of total channels, then save to file
% each field is in order of forloop in simCCEP_testNChs: nchs x responsiveness x nReps
accuracy = struct();
accuracy.TP = nan(length(nChsToTest), length(fracResp), nReps);
accuracy.TN = nan(length(nChsToTest), length(fracResp), nReps);
accuracy.FN = nan(length(nChsToTest), length(fracResp), nReps);
accuracy.FP = nan(length(nChsToTest), length(fracResp), nReps);
accuracy.TPglob = nan(length(nChsToTest), length(fracResp), nReps); % for the global maxes
accuracy.TNglob = nan(length(nChsToTest), length(fracResp), nReps);
accuracy.FNglob = nan(length(nChsToTest), length(fracResp), nReps);
accuracy.FPglob = nan(length(nChsToTest), length(fracResp), nReps);
% First store the 50 ch condition by pulling from previous output folder
for nR = 1:length(fracResp)
fprintf('.');
nResp = round(fracResp(nR)*50);
indir = fullfile('output', rootdir50, sprintf('nchs50-%d', nResp));
for rep = 1:nReps
T_acc = readtable(fullfile(indir, sprintf('accuracy_50-%d_rep%d.txt', nResp, rep)), 'FileType', 'text', 'Delimiter', '\t');
accuracy.TP(1, nR, rep) = T_acc.Var2(strcmp(T_acc.Var1, 'TP'));
accuracy.TN(1, nR, rep) = T_acc.Var2(strcmp(T_acc.Var1, 'TN'));
accuracy.FN(1, nR, rep) = T_acc.Var2(strcmp(T_acc.Var1, 'FN'));
accuracy.FP(1, nR, rep) = T_acc.Var2(strcmp(T_acc.Var1, 'FP'));
accuracy.TPglob(1, nR, rep) = T_acc.Var2(strcmp(T_acc.Var1, 'TPglob'));
accuracy.TNglob(1, nR, rep) = T_acc.Var2(strcmp(T_acc.Var1, 'TNglob'));
accuracy.FNglob(1, nR, rep) = T_acc.Var2(strcmp(T_acc.Var1, 'FNglob'));
accuracy.FPglob(1, nR, rep) = T_acc.Var2(strcmp(T_acc.Var1, 'FPglob'));
end
end
% now load for each total number of channels from rootdir
for nC = 2:length(nChsToTest)
for nR = 1:length(fracResp)
fprintf('.');
nChs = nChsToTest(nC);
indir = fullfile('output', rootdir, sprintf('nchs%d-%d', nChs, round(fracResp(nR)*nChs)));
for rep = 1:nReps
T_acc = readtable(fullfile(indir, sprintf('accuracy_rep%d.txt', rep)), 'FileType', 'text', 'Delimiter', '\t');
accuracy.TP(nC, nR, rep) = T_acc.Var2(strcmp(T_acc.Var1, 'TP'));
accuracy.TN(nC, nR, rep) = T_acc.Var2(strcmp(T_acc.Var1, 'TN'));
accuracy.FN(nC, nR, rep) = T_acc.Var2(strcmp(T_acc.Var1, 'FN'));
accuracy.FP(nC, nR, rep) = T_acc.Var2(strcmp(T_acc.Var1, 'FP'));
accuracy.TPglob(nC, nR, rep) = T_acc.Var2(strcmp(T_acc.Var1, 'TPglob'));
accuracy.TNglob(nC, nR, rep) = T_acc.Var2(strcmp(T_acc.Var1, 'TNglob'));
accuracy.FNglob(nC, nR, rep) = T_acc.Var2(strcmp(T_acc.Var1, 'FNglob'));
accuracy.FPglob(nC, nR, rep) = T_acc.Var2(strcmp(T_acc.Var1, 'FPglob'));
end
end
end
fprintf('\n');
save(fullfile('output', rootdir, 'accuracies_all.mat'), 'accuracy');
%% Plot FN, FP vs resp traces for each noise level, for first-peak method, as bars
load(fullfile('output', rootdir, 'accuracies_all.mat'), 'accuracy');
% displacements for the bars
disps = [-0.04, -0.02, 0, 0.02, 0.04];
% Based on more sensitive, first-peak optimum
figure('Position', [200, 200, 700, 800]);
subplot(2, 1, 1); % FN = responsive channels included in the CAR
hold on
for nc = 1:size(accuracy.FN, 1) % starting at 2 excludes the 50
FN = squeeze(accuracy.FN(nc, :, :));
FN = FN ./ (fracResp'*nChsToTest(nc)); % normalize by number of responsive channels
FN(isnan(FN)) = 0; % when there are 0 responsive channels, change these nans to 0
iqr = prctile(FN, [25, 75], 2);
med = median(FN, 2);
bar(fracResp + disps(nc), med, 0.08, 'FaceColor', parCm(nc*3-2, :));
errorbar(fracResp + disps(nc), med, med - iqr(:, 1), iqr(:, 2) - med, 'Color', parCm(nc*3-2, :), 'LineStyle', 'none', 'LineWidth', 1, 'CapSize', 8);
end
hold off
xlim([-0.05, 0.85]); ylim([-0.05, 1.05]);
set(gca, 'xtick', 0:0.2:0.8, 'xticklabels', 0:20:80, 'ytick', 0:0.2:1, 'yticklabels', 0:20:100, 'box', 'off');
ylabel('% Responsive Channels Missed');
subplot(2, 1, 2);
hold on
for nc = 1:size(accuracy.FN, 1)
FP = squeeze(accuracy.FP(nc, :, :));
FP = FP ./ ((1-fracResp)'*nChsToTest(nc)); % normalize by number of non-responsive channels
iqr = prctile(FP, [25, 75], 2);
med = median(FP, 2);
bar(fracResp + disps(nc), med, 0.08, 'FaceColor', parCm(nc*3-2, :));
errorbar(fracResp + disps(nc), med, med - iqr(:, 1), iqr(:, 2) - med, 'Color', parCm(nc*3-2, :), 'LineStyle', 'none', 'LineWidth', 1, 'CapSize', 8);
end
hold off
xlim([-0.05, 0.85]); ylim([-0.05, 1.05]);
set(gca, 'xtick', 0:0.2:0.8, 'xticklabels', 0:20:80, 'ytick', 0:0.2:1, 'yticklabels', 0:20:100, 'box', 'off');
xlabel('Fraction of Channels with Response');
ylabel('% Non-responsive Channels Missed');
saveas(gcf, fullfile('output', rootdir, 'FNFP_bars_acrossNChs'), 'png');
saveas(gcf, fullfile('output', rootdir, 'FNFP_bars_acrossNChs'), 'svg');