No 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 highpower 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.
No abstract
Fast IPv4 scanning has enabled researchers to answer a wealth of security and networking questions. Yet, despite widespread use, there has been little validation of the methodology's accuracy, including whether a single scan provides sufficient coverage. In this paper, we analyze how scan origin affects the results of Internet-wide scans by completing three HTTP, HTTPS, and SSH scans from seven geographically and topologically diverse networks. We find that individual origins miss an average 1.6-8.4% of HTTP, 1.5-4.6% of HTTPS, and 8.3-18.2% of SSH hosts. We analyze why origins see different hosts, and show how permanent and temporary blocking, packet loss, geographic biases, and transient outages affect scan results. We discuss the implications for scanning and provide recommendations for future studies.
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