Measuring the average power of neural oscillations
Bibliographic record
Abstract
Abstract Background 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. New method We 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. Results 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 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. Comparison with existing methods In 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. Conclusions These results call into question the validity of common statistical practices in neural oscillation research. Highlights Analyses of oscillatory power often assume power is normally distributed. Analyzing >40 hours of human M/EEG and ECoG, we show that in all cases it is not. This effect is demonstrated in simple simulation of an event-related task. Overinflated power estimates are reduced via log-transformation or median power.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".