Using pwelch to a set of signals: some questions (

Question

I would like to use pwelch on a set of signals and I have some questions.

First, let's say that we have 32 (EEG) signals of 30 seconds duration. The sampling frequency is fs=256 samples/sec, and thus each signal has length 7680. I would like to use pwelch in order to estimate the power spectral density (PSD) of those signals.

Question 1: Based on the pwelch's documentation,

pxx = pwelch(x) returns the power spectral density (PSD) estimate, pxx, of the input signal, x, found using Welch's overlapped segment averaging estimator. When x is a vector, it is treated as a single channel. When x is a matrix, the PSD is computed independently for each column and stored in the corresponding column of pxx.

However, if call pwelch as follows

% ch_signals: 7680x32; one channel signal per each column
[pxx,f] = pwelch(ch_signals);

the resulting pxx is of size 1025x1, not 1025x32 as I would expect, since the documentation states that if x is a matrix the PSD is computed independently for each column and stored in the corresponding column of pxx.

Question 2: Let's say that I overcome this problem, and I compute the PSD of each signal independently (by applying pwelch to each column of ch_signals), I would like to know what is the best way of doing so. Granted that the signal is a 30-second signal in time with sampling frequency fs=256, how should I call pwelch (with what arguments?) such that the PSD is meaningful?

Question 3: If I need to split each of my 32 signals into windows and apply pwech to each one of those windows, what would be the best approach? Let's say that I would like to split each of my 30-second signals into windows of 3 seconds with an overlap of 2 seconds. How should I call pwelch for each one of those windows?

Answer 1

Here is an example, just like your case,

The results show that the algorithm indicates the signal frequencies just right.

Each column of matrix, y is a sinusoidal to check how it works.

The windows are 3 seconds with 2 seconds of overlapping,

Fs = 256;                       
T = 1/Fs;                    
t = (0:30*Fs-1)*T;           
y = sin(2 * pi * repmat(linspace(1,100,32)',1,length(t)).*repmat(t,32,1))';
for i = 1 : 32
[pxx(:,i), freq] = pwelch(y(:,i),3*Fs,2*Fs,[],Fs); %#ok
end
plot(freq,pxx);
xlabel('Frequency (Hz)'); 
ylabel('Spectral Density (Hz^{-1})');