Abstract
Neural oscillations are often quantified as average power relative to a cognitive, perceptual, and/or behavioral task. This is commonly done using Fourier-based techniques, such as Welch’s method for estimating the power spectral density, and/or by estimating narrowband oscillatory power across trials, conditions, and/or groups. The core assumption underlying these approaches is that the mean is an appropriate measure of central tendency. Despite the importance of this assumption, it has not been rigorously tested in real neural data. Analyzing 101 participants’ worth of human electrophysiology, totaling 3,560 channels and over 40 hours data, we show that, in all cases examined, spectral power is not Gaussian distributed. This holds true even in when oscillations are more prominent and sustained, such as with visual cortical alpha. We find that power across time, at every frequency, is characterized by a substantial long tail, which implies that most estimates of average spectral power are skewed toward the largest, most infrequent high-power oscillatory bursts. That is, oscillatory power is unstable in time, characterized by long periods of low power with infrequent periods of higher power. In a simulated event-related experiment we show how the introduction of just a few high-power oscillatory bursts, as seen in real data, can, perhaps erroneously, cause significant differences between conditions. These results call into question the validity of common statistical practices in neural oscillation research. We suggest other approaches that are better suited for the physiological reality of how neural oscillations often manifest: as nonstationary, high-power bursts, rather than sustained rhythms.
Author contributions
All authors initiated and designed the study. Izhikevich built the database and analysis pipeline, and all authors analyzed the data. All authors contributed to the manuscript.
Acknowledgements
Izhikevich is supported by the National Science Foundation Graduate Research Fellowship Program (DGE-1656518) and a Stanford Graduate Fellowship. Gao is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC PGS-D), UCSD Kavli Innovative Research Grant (IRG), and the Katzin Prize. Voytek is supported by a Sloan Research Fellowship (FG-2015-66057), the Whitehall Foundation (2017-12-73), and the National Science Foundation under grant BCS-1736028. The authors declare no competing financial interests.