AI Article Synopsis
- Spectral analysis is a common technique for estimating 1/f(α) noise in behavioral and physiological data, but sampling rate affects the results due to lower amplitude fluctuations at higher frequencies.
- The paper proposes a new analytical approach that maintains a consistent amount of low frequencies in power spectra, making estimates of 1/f(α) noise less sensitive to changes in sample rates.
- This research aims to clarify existing inconsistencies in the psychological literature regarding 1/f(α) noise and establish a more reliable framework for studying this phenomenon in continuous processes.
Article Abstract
Spectral analysis is a widely used method to estimate 1/f(α) noise in behavioral and physiological data series. The aim of this paper is to achieve a more solid appreciation for the effects of periodic sampling on the outcomes of spectral analysis. It is shown that spectral analysis is biased by the choice of sample rate because denser sampling comes with lower amplitude fluctuations at the highest frequencies. Here we introduce an analytical strategy that compensates for this effect by focusing on a fixed amount, rather than a fixed percentage of the lowest frequencies in a power spectrum. Using this strategy, estimates of the degree of 1/f(α) noise become robust against sample rate conversion and more sensitive overall. Altogether, the present contribution may shed new light on known discrepancies in the psychological literature on 1/f(α) noise, and may provide a means to achieve a more solid framework for 1/f(α) noise in continuous processes.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3549522 | PMC |
| http://dx.doi.org/10.3389/fphys.2012.00495 | DOI Listing |
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