Measuring the average power of neural oscillations

Abstract

BackgroundNeural 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 Welchs 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.\n\nNew methodWe introduce extensions of common approaches that are better suited for the physiological reality of how neural oscillations often manifest: as nonstationary, high-power bursts, rather than sustained rhythms. Log-transforming, or taking the median power, significantly reduces erroneously inflated power estimates.\n\nResultsAnalyzing 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 is true even when oscillations are prominent and sustained, such as visual cortical alpha. Power across time, at every frequency, is characterized by a substantial long tail, which implies that estimates of average power are skewed toward large, infrequent high-power oscillatory bursts.\n\nComparison with existing methodsIn a simulated event-related experiment we show how introducing just a few high-power oscillatory bursts, as seen in real data, can, perhaps erroneously, cause significant differences between conditions using traditional methods. These erroneous effects are substantially reduced with our new methods.\n\nConclusionsThese results call into question the validity of common statistical practices in neural oscillation research.\n\nHighlightsO_LIAnalyses of oscillatory power often assume power is normally distributed.\nC_LIO_LIAnalyzing >40 hours of human M/EEG and ECoG, we show that in all cases it is not.\nC_LIO_LIThis effect is demonstrated in simple simulation of an event-related task.\nC_LIO_LIOverinflated power estimates are reduced via log-transformation or median power.\nC_LI