Power Spectra estimation after FFT
5 vues (au cours des 30 derniers jours)
I am working on a frequency analysis for skin conductance. The frequencies expected on those kinds of signals are very low (between 0.045 and 0.25 Hz), so I would like to get the PSD in specific low-frequency ranges, i.e. (0 - 0.045), (0.045 - 0.15), (0.15 - 0.25), and the total power of the signal. To do so, I computed the Fast Fourier Transform, but now the problem is computing the power spectrum after the fourier.
The tutorials from Matlab have not been of much help, and also there are so many functions that can be used that I'm feeling a little lost at the moment! Can anyone help me work out this issue? Among the many options, which one do you think works best, in terms of both efficacy and computational time? I'm not looking for anything too fancy really, just an efficacious and standard protocol.
The processing pipeline I am following is:
- 8th order Chebyshev Type I lowpass filter, with 0.8 cutoff
- Downsampling from 4 to 2 Hz (factor = 2)
- 8th order Butterworth highpass filter with 0.01 cutoff (do you think is appropriate, given that I'm mostly interested in low frequencies?)
- Welch's periodogram (50% overlap)
- Blackman window (128 points) applied to each segment
- FFT computer on each windowed segment
I also attached the code because it would be amazing to have examples or suggestions about how to proceed based on real examples! Any advice is always appreciated!
Thank you very much to any of you who is willing to help!
LO le 13 Juil 2020
try this example:
sampling_freq = 5000
window = 8192,
noverlap = 4096,
nfft = 8192,
[p,f] = pwelch(your_signal, 8192,4096,8192,5000);
max_power=10*log10(max(p)); % this would be the max power
plot(f,10*log10(p)); % this would be the power plot