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

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.

[CalebJones]

Image Analyst help!

Answer 1

[Meg Noah]

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');

[CalebJones]

Meg Noah Thank You so much

Now using newSpectrogram how do I rank the ICA_weights_right from low to high frequency?

[Meg Noah]

I made a mistake in interpretation. It's fixed above. You said there were 173 seconds of data. I had flipped the axes for frequency feature and time. It's now labeling the 173 seconds of data. What you have explained is the interpretation of the axis that has 100 samples, your 'feature' or 'frequency' axis. I don't know what frequencies correspond to the bins. Do you have more information? There should be some vector of 100 values for the '100 features from R-ICA' that presumably are given in Hz or rad/s. That is what is needed next. We can plot the significant features as functions of time. The cleaned up spectrogram is used to identify where the significan signals are.

totalAmplitude = sum(newSpectrogram');
idx = find(totalAmplitude > 2);
figure()
subplot(2,1,1)
for k = 1:length(idx)
    ifeature = idx(k);
    plot(t_s,ICA_weights_right(ifeature,:),'DisplayName',['Feature ' num2str(ifeature)]);
    hold on;
end
legend('location','best');
xlabel('time [s]');
ylabel('amplitude');
title('feature amplitude');
subplot(2,1,2)
for k = 1:length(idx)
    ifeature = idx(k);
    plot(t_s,newSpectrogram(ifeature,:),'DisplayName',['Feature ' num2str(ifeature)]);
    hold on;
end
legend('location','best');
xlabel('time [s]');
ylabel('amplitude');
title('feature amplitude after background noise removal');

[CalebJones]

Your awesome.

Thank You

[CalebJones]

Meg Noah

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?