read data and compute spectral density matrix in matlab

To compute spectral density matrix in matlab, we first need to read the data from a file. We can use the load function to do so, which takes the filename as input and returns the loaded data. In this example, we will assume the data is loaded into a variable called data.

main.m
data = load('data.txt');
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Next, we need to compute the spectral density matrix. We can use the pwelch function to compute the power spectral density estimates. The function takes the data and some parameters as input, such as the sampling frequency, window type, and window length. In this example, we will use a sampling frequency of 1000 Hz, a Hanning window, and a window length of 1024.

main.m
fs = 1000;      % Sampling frequency
win = hann(1024);   % Hanning window of length 1024

[S,f] = pwelch(data, win, [], [], fs);
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The result is a spectral density matrix S, containing the power spectral density estimates for each frequency bin, and a vector f, containing the corresponding frequency values. Each row of S represents a different frequency, and each column represents a different channel or sensor.

We can now plot the result using the imagesc function.

main.m
imagesc(S);
xlabel('Channel');
ylabel('Frequency (Hz)');
colorbar;
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This will create a color map of the spectral density matrix, where each color represents the power spectral density at a specific frequency and channel.