Power spectrum of short signals

AI Thread Summary
Calculating the power spectrum of short signals (200-500 ms) presents challenges, as standard methods like FFT yield inaccurate results for these durations. While FFT works well for longer segments, it struggles with short ones due to insufficient sampling of frequency components. The key issue is ensuring that each frequency component is sampled at least once per cycle to obtain reliable data. Users are seeking alternative algorithms optimized for short segments to improve accuracy. Understanding the relationship between segment length and the signal's bandwidth is crucial for effective analysis.
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I am looking for a way to calculate the power spectrum of short signals of variable duration (between 200 and 500 ms). Standard methods seem wildly inaccurate. Are there any tricks for short segments?

Thanks anyone for a response.
 
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If you have access to Matlab, you should be able to take the FFt of the signal, square it and get your power spectrum. Do a google search for Power Spectrum and Matlab and you'll get a load of hits.
 
For short signals?

Thank you Dr Transport. Yes I do have access to Matlab and FFT works great for segments longer than 500 ms. When the segments are short, fft does a very bad job. I am looking for some improved algorithm that may be better optimized for short segments. Any insights would be appreciated.
 
The length of the segment in ms does not matter. What matters is the length of the segment relative to the longest period of the signal of interest (i.e. the bandwidth of the signal).
You need at sample at least one entirely cycle of each frequency component you are trying to extract in order to get sensible results; my guess would be that you are hitting that "limit" when you go below 500 ms.
 
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