To put this in context, I'm looking to achieve both high speed and high accuracy for measuring biosignals through the FFT. I'll describe a quick example to see if I understand this properly as well as to illustrate my question. So, if I have this correctly, if I wanted to take a low-frequency FFT, say frequencies 0-64 Hz for example, to mmeet the Nyquist criterion my sampling frequency would have to be at least twice that, thus 128 Hz. Then, if I wanted a frequency resolution of 1 Hz to one bin, I would need 64 bins, which would put me at 128 samples because there are both real and imaginary parts. Therefore, to achieve that 1 Hz resolution, I would be presented with having a sampling rate of only 128 Hz while needing to take 128 samples, which would put me in the position of having only 1 full performance of the FFT for every second of time that elapses.
Ultimately, this leads me to my real question: at low frequencies, is it possible to take a high resolution FFT (say 1 or 2 Hz per bin) while still maintaining some semblance of speed? Or is this simply impossible due to the limitations of the transform? If so, is there some alternative method or some sort of compromise to be made between resolution and speed? As an aside, I read an article a while ago about oversampling and throwing out samples at certain integer multiples past a certain point (or something like that, sorry it was a little while ago) in order to speed up the sampling process. Maybe somebody knows what that is (or maybe I'm jut rambling at this point). Either way, thanks for any help you can provide in advance.
Best Answer
One usually needs to acquire multiple samples per waveform period to get good results from an FFT. The Nyquist limit of 2 samples per period is a lower bound but usually 10 samples per period or more is what is practically used. So to analyze a 64Hz signal you probably want to acquire samples at a rate of 640Hz or more.
Also (up to a point) you will get better results when measuring actual periodic signals if you acquire multiple waveform periods worth of samples. You will need to determine what window size makes the most sense for your application but to capture 1Hz signals I would suggest capturing somewhere around 10s worth of data.
So basically you need to acquire samples at a high rate relative to your highest frequency and for a long time relative to your lowest frequency to get good results. This mandates that there will be some sort of processing delay that will be a multiple of the period of your lowest frequency. This does not however prevent you from performing that processing as often as once every sample time.
So if you want to analyze the frequency components of a signal as it changes over time, and you want to see what the FFT looks like at a high rate then you can just take in however many samples you need. Run the FFT. Shift all the samples over 1 position, and then take the FFT again at the next sample time.
EXAMPLE:
1) Sample at 819.2 samples per second with a time window of 10 s.
2) Let samples accumulate for 10 s (for a total of 8192 samples)
3) Run the FFT on 8192 samples.
4) Discard the first sample in the buffer, and keep the other 8191 samples shifting them over 1 position.
5) 1/819.2 seconds later add in the next sample to the end of the buffer and re-run the FFT.
6) Repeat steps 4-6 until you have completed your analysis.
This would give you an FFT that analyzes a sliding window of data 819.2 times a second
The processing power needed for the example would be approximately 13*8192*819.2 Multiply-Accumulate opeations per second (87 Million MACs/s). An ordinary PC could easily handle this. You can of course reduce the processing power by a factor of N by only running the FFT ever N samples (for example running it every 8 samples only requies 11M MACs per second).