Zero-padding a Fast Fourier Transform (FFT) can increase the resolution of the frequency domain results (see FFT Zero Padding). This is useful when you are looking to determine something like a dominant frequency over a narrow band with limited data.

Here, I examine the case of a slow-wave (<4 Hz) signal. I am detecting the dominant frequency as determined by the power spectrum, and then examining how the phase changes as the FFT is zero-padded by a factor of n, where n=1 is no zero-padding.

Methods & Results

I used 10,000 trials of slow-wave data that were recorded at 250 Hz (each trial was 1,024 samples). These data were subject to a slow-wave IIR elliptical filter. For each trial, I padded the data by a factor n; if n=2, the 1,024 zeros are added to the original signal. Below shows a single trial and how changing n ‘smooths’ the power spectrum (middle) leading to a more accurate assessment of phase (bottom).

The plot below shows how different each pad factor was to the last pad iteration, wrapped to pi. This assumes the last pad iteration (e.g., n=10) is the most accurate estimation of the actual phase of the signal at the dominant frequency.

Interpretation & Use

The error in the phase of the FFT decreases as padding increases (i.e. converges), as expected, where n=10 is the trivial case and equals 0. The degree to which this matters in application-dependent—I don’t think a p-value really helps the interpretation, especially because these data are particular to the slow-wave use case. However, it is helpful to know how much of the data lies how far away.

This plot says that ~70% data have an ‘error’ greater than ±π/8 without any zero-padding (n=1) and decreases to ~50% for n=2. The practical question is really how much error is acceptable to your system. As put in MATLAB – FFT and Zero Padding, if you have the choice between zero-padding and including more data, always chose more data.