  I outline a signal resampling strategy for aligning event times between time
series trials in contexts where significant event times like onsets and offsets
vary between trials. These variations prevent direct comparisons of trials in
practical contexts as comparisons require equal-length time series (Salari et
al., 2019). Algorithms like dynamic time warping help us quantify these
variations locally but do not apply well to continuous transformations of time
series signals without interpolating or downsampling to add or remove samples
(Jamid, 2004; Eckner, 2014). I show that with consideration for padding and
sampling frequency that sinc interpolation is sufficient to resample parts of
trial intervals to produce equal-length time-locked trials that correlate to
and strongly approximate their unwarped counterparts with minimal interpolation
effects. Specifically I show that interpolation effects can be minimized by
oversampling, selectively interpolating mis-aligned parts of trials with
respect to mean mis-aligned event lengths, and interpolating mis-aligned events
with sufficient zero-padding. Interpolated signals then have a bandlimit well
below the Nyquist frequency and satisfy the Nyquist-Shannon sampling theorem
ensuring perfect reconstructions, and I find that I can track and potentially
counteract resampling effects on signal energy quantities.
