MATLAB: Mscohere function

coheremscohere

I'm using mscohere to calculate the coherence between two signals x, y. using the following formula: mscohere(x,y,window,overlap,nfft,sampling frequency)

1- I have the following questions: nfft doesn't permit to specify frequencies of interest i.e 1:1:50 Hz. Is there anyway to specify the frequencies of interest. When i use a frequency vector i receive an error message. 2- When i try to use the same function to analyze the coherence among 54 different signals (54 X 54 / 2 ) times. The processing time is considerably slow. Are there any tips or suggestions to make it faster. 3- Any suggestions for the ideal overlap and window duration ?

Thanks,

Best Answer

Hi Raf, You can't specify the frequencies of interest as an input argument, but you can easily extract the mean-square coherence values for the frequencies you want. This of course implies that you have chosen your input parameters to ensure that the spacing between DFT bins is sufficient.

t = 0:0.001:1-0.001;
x = cos(2*pi*10*t)+0.25*sin(2*pi*40*t)+randn(size(t));
y = cos(2*pi*10*t-pi/4)+0.15*sin(2*pi*40*t-pi/2)+randn(size(t));
[Cxy,F] = mscohere(x,y,250,200,250,1000);
plot(F,Cxy)

In the above, I've chosen a segment length of 250, that gives me a frequency resolution of Fs/250 or 4 Hz. You can see this by looking at

    diff(F)

If you really want a resolution of 1 Hz, then you have to choose your window length accordingly.

The overlap is trickier to give specific rules for. The larger the overlap, the more computation, but the more averaged the result (less bias)

Related Solutions

MATLAB: Is the unit of Cxy from cpsd in decibel

Hi, no, the output is the cross spectrum, it is complex-valued. If you want the output in db. Use

10*log10(abs(Pxy));

For example:

   Fs = 1000;  
   t = 0:1/Fs:.296;
   x = cos(2*pi*t*200)+randn(size(t)); 
   y = cos(2*pi*t*100)+randn(size(t));  
   [Pxy,F] = cpsd(x,y,[],[],[],Fs);  
   plot(F,10*log10(abs(Pxy))); grid on;
   % compare to 
   figure;
   cpsd(x,y,[],[],[],Fs);

MATLAB: Hi everybody, I have a question about coherence and mscohere function

The key thing for you is to choose a window length that allows you to ensure that your frequencies of interest fall directly on DFT bins. You have long time series, which is fortunate. By picking the segment (window) length equal to the sampling frequency, you have a spacing in the Fourier domain of 1 Hz. At the same time, you are able to get enough overlapped segments at that length to produce a good coherence estimate.

I've used an overlap of 2000 samples, but that can be played with a bit. Here is an example using your general problem where I simulate two signals, x and y.

   winlen = 2048;
   dt = 1/2048;
   t = 0:dt:(23203*dt)-dt;
   x = cos(2*pi*11*t)+sin(2*pi*70*t)+cos(2*pi*196*(t-pi/4))+sin(2*pi*384*(t-pi/8))+randn(size(t));
   y = 0.5*cos(2*pi*11*(t-pi/3))+2*sin(2*pi*70*t)+cos(2*pi*196*(t-3*pi/4))+sin(2*pi*384*(t-pi/4))+randn(size(t));
   [Cxy,F] = mscohere(x,y,hamming(winlen),2000,winlen,2048);
   plot(F,Cxy); xlabel('Hz'); ylabel('Magnitude-squared Coherence'); axis tight;

Best Answer

Related Solutions

MATLAB: Is the unit of Cxy from cpsd in decibel

MATLAB: Hi everybody, I have a question about coherence and mscohere function

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