时间扭曲试验
Time-warped Trials
摘要：
我概述了一个信号的响应策略，用于调整事件时间之间的时间序列试验的上下文中，显着的事件时间，如发作和偏移试验之间的变化。这些变化阻止了在实际环境中对试验的直接比较，因为比较需要等长的时间序列（Salari等人，2019年）。像动态时间规整这样的算法可以帮助我们局部量化这些变化，但不能很好地应用于时间序列信号的连续变换，而不需要插值或下采样来添加或删除样本（Jamid，2004; Eckner，2014）。我表明，考虑到填充和采样频率，sinc插值足以重新采样部分的试验间隔，以产生等长的时间锁定的试验，相关的，并强烈近似其未翘曲的同行，最小的插值效果。具体来说，我表明，插值效应可以最小化过采样，选择性地插入不对齐的部分试验的平均不对齐的事件长度，并插入不对齐的事件与足够的零填充。然后，插值信号的带宽限制远低于奈奎斯特频率，并满足奈奎斯特-香农采样定理，以确保完美的重建，我发现我可以跟踪和潜在的抵消信号能量的影响。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
