Power Spectral Density - how can I get a link back to the original signal

AI Thread Summary
To analyze a biological signal for periodicities using a Fourier transform, a windowed FFT approach is recommended, as a standard FFT does not allow for isolating specific frequencies. This involves segmenting the signal and applying FFT to each segment or using a moving window to observe how the spectrum changes over time. The Matlab signal processing toolbox provides necessary functions, but writing custom code from scratch is also feasible. The technique is formally known as the short-time Fourier transform, which can be explored further for implementation details. Understanding these methods will enable effective frequency distribution analysis of the signal.
Dr Ingend
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Hi everyone!

I would like to analyze a biological signal for periodicities using a Fourier transform, i.e., I'd like to get a frequency distribution plot based on a long stretch of signal. Additionally, I would like to be able to mark the positions of certain frequencies within the signal that give rise to a peak in the freq distribut plot.

Do you have any suggestions how to perform such an analysis?

Thanks in advance!
 
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If you did FFT of the whole signal, you cannot do this.
Each point of the FFT spectrum depends on all the points of the time domain signal.
You can do the so call "windowed" FFT when you cut your signal in several segments and do FFT transform for each. Or you use a mowing window and see how the spectrum evolves as you move the window.
 
Thank you! I thought about a moving window as well, but wasn't sure how to actually make it work. I think I would have to write it in Matlab from scratch.

Cheers
 

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