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clusterLFP.m
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function [clusterindx, LFP_cluster] = clusterLFP(cfg, MuseStruct, force_clustering)
% Neccecary fields:
% cfg.cluster.name
cfg.cluster.dbscan = ft_getopt(cfg.cluster, 'dbscan', 'no');
cfg.cluster.kmeans = ft_getopt(cfg.cluster, 'kmeans', 'no');
cfg.cluster.kmedoids = ft_getopt(cfg.cluster, 'kmedoids', 'no');
if ~any([strcmp(cfg.cluster.dbscan,'yes'), strcmp(cfg.cluster.kmeans,'yes'), strcmp(cfg.cluster.kmedoids,'yes')])
fprintf('No cluster methods requested');
return
end
cfg.cluster.reref = ft_getopt(cfg.cluster, 'reref', 'no');
cfg.cluster.refmethod = ft_getopt(cfg.cluster, 'refmethod', 'none');
cfg.cluster.ztransform = ft_getopt(cfg.cluster, 'ztransform', 'no');
cfg.cluster.dbscan = ft_getopt(cfg.cluster, 'dbscan', 'no');
cfg.cluster.epsilon = ft_getopt(cfg.cluster, 'epsilon', 1500);
cfg.cluster.MinPts = ft_getopt(cfg.cluster, 'MinPts', 10);
cfg.cluster.resamplefs = ft_getopt(cfg.cluster, 'resamplefs', []);
cfg.cluster.channels = ft_getopt(cfg.cluster, 'channels', 'all');
% load data if requested, and return
fname = fullfile(cfg.datasavedir,[cfg.prefix,'clusterindx.mat']);
if exist(fname,'file') && force_clustering == false
fprintf('********************************\n');
fprintf('** Loading results clustering **\n');
fprintf('********************************\n\n');
load(fname,'clusterindx','LFP_cluster');
return
end
% read LFP according to LFP settings in configuration
cfg.LFP.write = true;
LFP = readLFP(cfg, MuseStruct, false);
% preprocessing
for ipart = 1 : size(LFP,2)
for imarker = 1 : size(LFP{ipart},2)
if isempty(LFP{ipart}{imarker})
continue
end
% select only those channels and time periods requested
cfgtemp = [];
cfgtemp.channel = cfg.cluster.channels;
LFP{ipart}{imarker} = ft_selectdata(cfgtemp, LFP{ipart}{imarker});
% resample
if ~isempty(cfg.cluster.resamplefs)
cfgtemp = [];
cfgtemp.resamplefs = cfg.cluster.resamplefs;
LFP{ipart}{imarker} = ft_resampledata(cfgtemp,LFP{ipart}{imarker});
end
% filtering and baseline correction
cfgtemp = [];
cfgtemp.lpfilter = ft_getopt(cfg.cluster, 'lpfilter', 'no');
cfgtemp.hpfilter = ft_getopt(cfg.cluster, 'hpfilter', 'no');
cfgtemp.bpfilter = ft_getopt(cfg.cluster, 'bpfilter', 'no');
cfgtemp.bsfilter = ft_getopt(cfg.cluster, 'bsfilter', 'no');
cfgtemp.lpfreq = ft_getopt(cfg.cluster, 'lpfreq', []);
cfgtemp.hpfreq = ft_getopt(cfg.cluster, 'hpfreq', []);
cfgtemp.bpfreq = ft_getopt(cfg.cluster, 'bpfreq', []);
cfgtemp.bsfreq = ft_getopt(cfg.cluster, 'bsfreq', []);
cfgtemp.demean = ft_getopt(cfg.cluster, 'demean', 'no');
cfgtemp.baselinewindow = ft_getopt(cfg.cluster, 'baselinewindow', 'all');
LFP{ipart}{imarker} = ft_preprocessing(cfgtemp, LFP{ipart}{imarker});
% select only those time periods requested
cfgtemp = [];
cfgtemp.latency = ft_getopt(cfg.cluster, 'latency', 'all');
LFP{ipart}{imarker} = ft_selectdata(cfgtemp, LFP{ipart}{imarker});
% bipolar rereferencing if requested
if strcmp(cfg.cluster.reref, 'yes')
if strcmp(cfg.cluster.refmethod, 'bipolar')
labels_nonum = regexprep(LFP{ipart}{imarker}.label, '[0-9_]', '');
[~,~,indx] = unique(labels_nonum);
for i = 1 : max(indx)
cfgtemp = [];
cfgtemp.reref = 'yes';
cfgtemp.refmethod = 'bipolar';
cfgtemp.channel = LFP{ipart}{imarker}.label(indx==i);
group{i} = ft_preprocessing(cfgtemp,LFP{ipart}{imarker});
end
LFP{ipart}{imarker} = ft_appenddata([],group{:});
clear group
end
end
end
end
%%%%%%%%%%%%%%
%%% dbscan %%%
%%%%%%%%%%%%%%
if strcmp(cfg.cluster.dbscan, 'yes')
for ipart = 1 : size(LFP,2)
for imarker = 1 : size(LFP{ipart},2)
if isempty(LFP{ipart}{imarker})
continue
end
% put all in matrix with concatinated channels
d = size(LFP{ipart}{imarker}.trial{1});
LFP_concatinated = nan(size(LFP{ipart}{imarker}.trial,2), d(1)*d(2));
for itrial = 1 : size(LFP{ipart}{imarker}.trial, 2)
LFP_concatinated(itrial,:) = reshape(LFP{ipart}{imarker}.trial{itrial}', 1, d(1)*d(2));
end
% z-normalize data
if strcmp(cfg.cluster.ztransform, 'yes')
LFP_concatinated = nanznorm(LFP_concatinated);
LFP_concatinated(isnan(LFP_concatinated)) = 0;
end
for k = 2 : 40
[idx{k}, dist{k}] = knnsearch(LFP_concatinated, LFP_concatinated, 'K', k);
end
fig = figure('visible', 'off'); hold;
for k = 2 : 40
Y = sort(mean(dist{k}(:,2:end), 2));
plot(Y);
end
grid on
set(fig,'PaperOrientation','landscape');
set(fig,'PaperUnits','normalized');
set(fig,'PaperPosition', [0 0 1 1]);
print(fig, '-dpdf', fullfile(cfg.imagesavedir,[cfg.prefix, 'p', num2str(ipart), '_knn_', cfg.name{imarker}, '.pdf']), '-r600');
print(fig, '-dpng', fullfile(cfg.imagesavedir,[cfg.prefix, 'p', num2str(ipart), '_knn_', cfg.name{imarker}, '.png']), '-r600');
close all
[indx_dbscan, ~] = DBSCAN(LFP_concatinated, cfg.cluster.epsilon, cfg.cluster.MinPts);
indx_dbscan = indx_dbscan + 1;
clusterindx{ipart}{imarker}.dbscan = indx_dbscan;
fig = figure('visible', 'on'); hold;
fig.Renderer = 'Painters';
N = max(indx_dbscan);
i = 1;
for icluster = 1 : N
% realign data again per cluster
indx = indx_dbscan == icluster;
[LFP_concatinated_realigned, nshift] = alignXcorr(LFP_concatinated(indx,:), 10);
cfgtemp = [];
cfgtemp.trials = find(indx)';
LFP_shifted = ft_selectdata(cfgtemp, LFP{ipart}{imarker});
avg_original = ft_timelockanalysis(cfgtemp, LFP{ipart}{imarker});
for itrial = 1 : size(LFP_shifted.trial,2)
LFP_shifted.time{itrial} = LFP_shifted.time{itrial} + nshift(itrial) / LFP{ipart}{imarker}.fsample;
end
cfgtemp = [];
avg_shifted = ft_timelockanalysis(cfgtemp,LFP_shifted);
subplot(3,N,icluster); hold;
title(['Cluster ', num2str(icluster)]);
imagesc(LFP_concatinated(indx_kmeans==icluster, :));
set(gca,'xtick', []);
set(gca,'fontsize', 6)
axis tight
subplot(3,N,icluster+N*1); hold;
imagesc(LFP_concatinated_realigned);
set(gca,'xtick', []);
set(gca,'fontsize', 6)
axis tight
subplot(3,N,icluster+N*2); hold;
h = max(max(abs(avg_shifted.avg)));
n = 1;
ytick = [];
for ichan = 1 : size(avg_shifted.label,1)
ytick = [ytick, n*h];
plot(avg_original.time,avg_original.avg(ichan,:) + n*h, 'r');
plot(avg_shifted.time,avg_shifted.avg(ichan,:) + n*h, 'k');
temp = strsplit(avg_shifted.label{ichan}, '-');
avg_shifted.label{ichan} = temp{1};
n = n + 1;
end
yticks(ytick);
set(gca,'TickLabelInterpreter', 'none')
set(gca,'fontsize', 6)
yticklabels(avg_shifted.label);
xlabel('Time (s)');
axis tight
xlim([-0.5 1]);
box off
LFP_cluster{ipart}{imarker}.dbscan{icluster} = sel;
end
set(fig,'PaperOrientation','landscape');
set(fig,'PaperUnits','normalized');
set(fig,'PaperPosition', [0 0 1 1]);
print(fig, '-dpdf', fullfile(cfg.imagesavedir, [cfg.prefix, 'p', num2str(ipart), '_DBSCAN_', cfg.name{imarker}, '.pdf']), '-r600');
print(fig, '-dpng', fullfile(cfg.imagesavedir, [cfg.prefix, 'p', num2str(ipart), '_DBSCAN_', cfg.name{imarker}, '.png']), '-r600');
close all
end
end
end
%%%%%%%%%%%%%%
%%% kmeans %%%
%%%%%%%%%%%%%%
if strcmp(cfg.cluster.kmeans, 'yes')
for ipart = 1 : size(LFP,2)
for imarker = 1 : size(LFP{ipart},2)
if isempty(LFP{ipart}{imarker})
continue
end % put all in matrix with concatinated channels
d = size(LFP{ipart}{imarker}.trial{1});
LFP_concatinated = nan(size(LFP{ipart}{imarker}.trial,2),d(1)*d(2));
for itrial = 1 : size(LFP{ipart}{imarker}.trial,2)
LFP_concatinated(itrial,:) = reshape(LFP{ipart}{imarker}.trial{itrial}',1,d(1)*d(2));
end
% z-normalize data
if strcmp(cfg.cluster.ztransform, 'yes')
LFP_concatinated = nanznorm(LFP_concatinated);
LFP_concatinated(isnan(LFP_concatinated)) = 0;
end
for N = cfg.cluster.N
[indx_kmeans, ~] = kmeans(LFP_concatinated, N, 'MaxIter', 2000, 'Replicates', 100, 'dist', 'sqeuclidean');
clusterindx{ipart}{imarker}.kmeans(:,N) = indx_kmeans;
% plot kmeans clusters
fig = figure('visible', 'on');
fig.Renderer = 'Painters';
for icluster = 1 : N
% realign data again per cluster
indx = indx_kmeans == icluster;
[LFP_concatinated_realigned, nshift] = alignXcorr(LFP_concatinated(indx,:), 10);
cfgtemp = [];
cfgtemp.trials = find(indx)';
LFP_shifted = ft_selectdata(cfgtemp, LFP{ipart}{imarker});
avg_original = ft_timelockanalysis(cfgtemp, LFP{ipart}{imarker});
for itrial = 1 : size(LFP_shifted.trial,2)
LFP_shifted.time{itrial} = LFP_shifted.time{itrial} + nshift(itrial) / LFP{ipart}{imarker}.fsample;
end
cfgtemp = [];
avg_shifted = ft_timelockanalysis(cfgtemp,LFP_shifted);
subplot(3,N,icluster); hold;
if icluster == 1
title(['Noise']);
else
title(['Cluster ', num2str(icluster-1)]);
end
imagesc(LFP_concatinated(indx_kmeans==icluster, :));
set(gca,'xtick', []);
set(gca,'fontsize', 6)
axis tight
subplot(3,N,icluster+N*1); hold;
imagesc(LFP_concatinated_realigned);
set(gca,'xtick', []);
set(gca,'fontsize', 6)
axis tight
subplot(3,N,icluster+N*2); hold;
h = max(max(abs(avg_shifted.avg)));
n = 1;
ytick = [];
for ichan = 1 : size(avg_shifted.label,1)
ytick = [ytick, n*h];
plot(avg_original.time,avg_original.avg(ichan,:) + n*h, 'r');
plot(avg_shifted.time,avg_shifted.avg(ichan,:) + n*h, 'k');
temp = strsplit(avg_shifted.label{ichan}, '-');
avg_shifted.label{ichan} = temp{1};
n = n + 1;
end
yticks(ytick);
set(gca,'TickLabelInterpreter', 'none')
set(gca,'fontsize', 6)
yticklabels(avg_shifted.label);
xlabel('Time (s)');
axis tight
xlim([-0.5 1]);
box off
% save for output
LFP_cluster{ipart}{imarker}.kmeans{N}{icluster} = avg_shifted;
end
set(fig,'PaperOrientation', 'landscape');
set(fig,'PaperUnits', 'normalized');
set(fig,'PaperPosition', [0 0 1 1]);
print(fig, '-dpdf', fullfile(cfg.imagesavedir, [cfg.prefix, 'p' ,num2str(ipart), '_kmeans_', cfg.name{imarker}, '_N', num2str(N), '.pdf']), '-r600');
print(fig, '-dpng', fullfile(cfg.imagesavedir, [cfg.prefix, 'p' ,num2str(ipart), '_kmeans_', cfg.name{imarker}, '_N', num2str(N), '.png']), '-r600');
close all
% plot siluette
fig = figure('visible', 'off'); hold;
[silh,h] = silhouette(LFP_concatinated, indx_kmeans, 'sqeuclidean');
set(fig,'PaperOrientation', 'landscape');
set(fig,'PaperUnits', 'normalized');
set(fig,'PaperPosition', [0 0 1 1]);
print(fig, '-dpdf', fullfile(cfg.imagesavedir, [cfg.prefix, 'p' ,num2str(ipart), '_kmeans_', cfg.name{imarker}, '_N', num2str(N), '_silhouette.pdf']));
print(fig, '-dpng', fullfile(cfg.imagesavedir, [cfg.prefix, 'p' ,num2str(ipart), '_kmeans_', cfg.name{imarker}, '_N', num2str(N), '_silhouette.png']));
close all
end
end
end
end
%%%%%%%%%%%%%%%%
%%% kmedoids %%%
%%%%%%%%%%%%%%%%
if strcmp(cfg.cluster.kmedoids, 'yes')
for ipart = 1 : size(LFP,2)
for imarker = 1 : size(LFP{ipart},2)
if isempty(LFP{ipart}{imarker})
continue
end
% put all in matrix with concatinated channels
d = size(LFP{ipart}{imarker}.trial{1});
LFP_concatinated = nan(size(LFP{ipart}{imarker}.trial,2),d(1)*d(2));
for itrial = 1 : size(LFP{ipart}{imarker}.trial,2)
LFP_concatinated(itrial,:) = reshape(LFP{ipart}{imarker}.trial{itrial}',1,d(1)*d(2));
end
% z-normalize data
if strcmp(cfg.cluster.ztransform, 'yes')
LFP_concatinated = nanznorm(LFP_concatinated);
LFP_concatinated(isnan(LFP_concatinated)) = 0;
end
for N = cfg.cluster.N
[indx_kmedoids, ~] = kmedoids(LFP_concatinated, N);
clusterindx{ipart}{imarker}.kmedoids(:,N) = indx_kmedoids;
% plot kmedoids clusters
fig = figure('visible', 'on');
fig.Renderer = 'Painters';
for icluster = 1 : N
% realign data again per cluster
indx = indx_kmedoids == icluster;
[LFP_concatinated_realigned, nshift] = alignXcorr(LFP_concatinated(indx,:), 10);
cfgtemp = [];
cfgtemp.trials = find(indx)';
LFP_shifted = ft_selectdata(cfgtemp, LFP{ipart}{imarker});
avg_original = ft_timelockanalysis(cfgtemp, LFP{ipart}{imarker});
for itrial = 1 : size(LFP_shifted.trial,2)
LFP_shifted.time{itrial} = LFP_shifted.time{itrial} + nshift(itrial) / LFP{ipart}{imarker}.fsample;
end
% LFP_shifted = rmfield(LFP_shifted,'cfg');
cfgtemp = [];
avg_shifted = ft_timelockanalysis(cfgtemp,LFP_shifted);
subplot(3, N, icluster); hold;
title(['Cluster ', num2str(icluster)]);
imagesc(LFP_concatinated(indx_kmedoids==icluster, :));
set(gca,'xtick', []);
set(gca,'fontsize', 6)
axis tight
subplot(3, N, icluster+N*1); hold;
imagesc(LFP_concatinated_realigned);
set(gca,'xtick', []);
set(gca,'fontsize', 6)
axis tight
subplot(3, N, icluster+N*2); hold;
h = max(max(abs(avg_shifted.avg)));
n = 1;
ytick = [];
for ichan = 1 : size(avg_shifted.label,1)
ytick = [ytick, n*h];
plot(avg_original.time,avg_original.avg(ichan,:) + n*h, 'r');
plot(avg_shifted.time,avg_shifted.avg(ichan,:) + n*h, 'k');
temp = strsplit(avg_shifted.label{ichan}, '-');
label{ichan} = temp{1};
n = n + 1;
end
yticks(ytick);
set(gca,'TickLabelInterpreter', 'none')
set(gca,'fontsize', 6)
yticklabels(label);
xlabel('Time (s)');
axis tight
xlim([-0.5 1]);
box off
% save for output
LFP_cluster{ipart}{imarker}.kmedoids{N}{icluster} = avg_shifted;
end
set(fig,'PaperOrientation', 'landscape');
set(fig,'PaperUnits', 'normalized');
set(fig,'PaperPosition', [0 0 1 1]);
print(fig, '-dpdf', fullfile(cfg.imagesavedir, [cfg.prefix, 'p' ,num2str(ipart), '_kmedoids_', cfg.name{imarker}, '_N', num2str(N), '.pdf']), '-r600');
print(fig, '-dpng', fullfile(cfg.imagesavedir, [cfg.prefix, 'p' ,num2str(ipart), '_kmedoids_', cfg.name{imarker}, '_N', num2str(N), '.png']), '-r600');
close all
% plot siluette
fig = figure('visible', 'off'); hold;
[silh,h] = silhouette(LFP_concatinated, indx_kmedoids, 'sqeuclidean');
set(fig,'PaperOrientation', 'landscape');
set(fig,'PaperUnits', 'normalized');
set(fig,'PaperPosition', [0 0 1 1]);
print(fig, '-dpdf', fullfile(cfg.imagesavedir, [cfg.prefix, 'p' ,num2str(ipart), '_kmedoids_', cfg.name{imarker}, '_N', num2str(N), '_silhouette.pdf']));
print(fig, '-dpng', fullfile(cfg.imagesavedir, [cfg.prefix, 'p' ,num2str(ipart), '_kmedoids_', cfg.name{imarker}, '_N', num2str(N), '_silhouette.png']));
close all
end
end
end
end
save(fname, 'clusterindx', 'LFP_cluster');
% %% Dynamic Time Warping
%
% maxamp = 100;
% tic;
%
% if maxamp == 0
% dtwD = pdist(z_input,@(Xi,Xj) dtwdist(Xi,Xj,'squared'));
% else
% dtwD = pdist(z_input,@(Xi,Xj) dtwdist(Xi,Xj,maxamp,'squared'));
% end
%
% save(fullfile(cfg.datasavedir,[cfg.prefix,'dtwD_sel.mat']),'dtwD');
% elapsedTime = toc
%
% fig = figure;
% fig.Renderer = 'Painters';
% imagesc(squareform(dtwD));
% colorbar;
% title('Distance Matrix');
% set(fig,'PaperOrientation','landscape');
% set(fig,'PaperUnits','normalized');
% set(fig,'PaperPosition', [0 0 1 1]);
% print(fig, '-dpdf', fullfile(cfg.imagesavedir,[cfg.prefix,'distance_matrix_DTW_',cfg.name{imarker},'_N',num2str(N),'.pdf']),'-r600');
% print(fig, '-dpng', fullfile(cfg.imagesavedir,[cfg.prefix,'distance_matrix_DTW_',cfg.name{imarker},'_N',num2str(N),'.png']),'-r600');
% close all
%
%
% % plot tree
%
% clustTreeEuc = linkage(dtwD,'weighted');
% clear sumc
%
% fig = figure;
% fig.Renderer = 'Painters';
%
% subplot(1,3,1);
% [h,nodes] = dendrogram(clustTreeEuc,0);
% title(sprintf('Cophenetic corr: %0.2f',cophenet(clustTreeEuc,eucD)));
% set(gca,'xtick',[]);
%
% subplot(1,3,2);
% [h,nodes] = dendrogram(clustTreeEuc,10);
% title('Max leafs: 10');
%
% subplot(1,3,3);
% [h,nodes] = dendrogram(clustTreeEuc,N);
% title(sprintf('Max leafs: %d',N));
% xlabel('Leaf number');
%
% for i = 1 : N
% sumc(i) = sum(nodes==i);
% end
% set(gca,'Xticklabel',sumc);
% xlabel('#Datapoints');
%
% % for i = 1 : N
% % cluster{i} = input(nodes==i,:);
% % cluster_mean(i,:) = mean(cluster{i});
% % end
% % ymin = min(min(cluster_mean));
% % ymax = max(max(cluster_mean));
% %
%
% set(fig,'PaperOrientation','landscape');
% set(fig,'PaperUnits','normalized');
% set(fig,'PaperPosition', [0 0 1 1]);
% print(fig, '-dpdf', fullfile(cfg.imagesavedir,[cfg.prefix,'dendogram_DTW_',cfg.name{imarker},'.pdf']),'-r600');
% print(fig, '-dpng', fullfile(cfg.imagesavedir,[cfg.prefix,'dendogram_DTW_',cfg.name{imarker},'.png']),'-r600');
% close all
%
% % plot grids
% fig = figure;
% fig.Renderer = 'Painters';
%
% for i = 1 : N
%
% [z_input_realigned,~] = align_data(z_input(indx==i,:),5);
%
% subplot(3,N,i);
% imagesc(z_input(indx==i,:));
% set(gca,'xtick',[]);
%
% subplot(3,N,i+N*1);
% imagesc(z_input_realigned);
% set(gca,'xtick',[]);
%
% subplot(3,N,i+N*2); hold;
% plot(dat{imarker}.time{1},nanmean(z_input(indx==i,:)));
% % xlim([dat{imarker}.time{1}(1),dat{imarker}.time{1}(end)]);
% % ylim([ymin*1.1,ymax*1.1]);
% axis tight
% plot(dat{imarker}.time{1},nanmean(z_input_realigned));
%
% end
% % subplot(3,N,N*2+1);
% % [silh,h] = silhouette(shifted_clean_z,nodes,'euclidean');
%
% set(fig,'PaperOrientation','landscape');
% set(fig,'PaperUnits','normalized');
% set(fig,'PaperPosition', [0 0 1 1]);
% print(fig, '-dpdf', fullfile(cfg.imagesavedir,[cfg.prefix,'hierach_clusters_DTW_',cfg.name{imarker},'_N',num2str(N),'.pdf']),'-r600');
% print(fig, '-dpng', fullfile(cfg.imagesavedir,[cfg.prefix,'hierach_clusters_DTW_',cfg.name{imarker},'_N',num2str(N),'.png']),'-r600');
% close all
%