How to sort my signal based on the frequency domain? low to high

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So i have 100 features from R-ICA. Some of the signals are very high frequency and a few, say 11 out of 100 are low frequency.
Is there a way to rank the signals based on the frequency domain.
I have attched a matfile with 100 features obtained from ICA.
173 is my activity window.
Each task is 173 seconds long and 100 is the ICA features.

Accepted Answer

Meg Noah
Meg Noah on 9 Jan 2020
Edited: Meg Noah on 9 Jan 2020
Here's a suggestion for enhancing the spectogram to pull out signals. More information about the feature signals would be helpful for extracting the energy at each timestep.
load('matlab.mat');
ICA_weights_right(ICA_weights_right<0) = 0;
t_s = 1:173;
f = 1:100;
% find the timesteps that have a strong signal - zero out the ones that do
% not have a strong signal
maskSpectrogram = ICA_weights_right;
zerolength = 20;
for ifeature = 1:100
tmp = zeros(173,1);
tmp(ICA_weights_right(:,ifeature)==0) = 1;
tmpLabeled = bwlabel(tmp);
stats = regionprops(tmpLabeled,'Area');
idx = find([stats.Area] > zerolength);
for k = 1:length(idx)
maskSpectrogram(tmpLabeled == idx(k),ifeature) = 1;
end
tmp = zeros(173,1);
tmp(ICA_weights_right(:,ifeature)>0) = 1;
tmpLabeled = bwlabel(tmp);
stats = regionprops(tmpLabeled,'Area');
idx = find([stats.Area] > zerolength);
for k = 1:length(idx)
maskSpectrogram(tmpLabeled == idx(k),ifeature) = 1;
end
end
newSpectrogram = ICA_weights_right.*maskSpectrogram;
mask5x1 = ones(5,1);
newSpectrogram = imopen(newSpectrogram,mask5x1);
ICA_weights_right = ICA_weights_right'
newSpectrogram = newSpectrogram'
figure('color','white');
subplot(2,1,1)
imagesc(t_s,f,ICA_weights_right);
xlabel('time [s]');
ylabel('feature (frequency) index');
set(gca,'ydir','normal');
set(gca,'fontweight','bold','fontsize',14);
title('Spectrogram');
subplot(2,1,2)
imagesc(t_s,f,newSpectrogram);
xlabel('time [s]');
ylabel('feature (frequency) index');
set(gca,'ydir','normal');
set(gca,'fontweight','bold','fontsize',14);
title('Post-Processed Spectrogram');
spectrogram.png
  4 Comments
CalebJones
CalebJones on 7 Feb 2020
This approach was very helpful. And it has improved my research work by a big margin, however, is it possible to sort the entire feature space from low to high?
So that I can choose first 10 which is low freq and last 10 which is high freq.
What way the code can be tweaked to produce such an output?

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