BOSC: A better oscillation detection method, extracts both sustained and transient rhythms from rat hippocampal recordings
Bibliographic record
Abstract
Neuronal population oscillations at a variety of frequencies can be readily seen in electroencephalographic (EEG) as well as local field potential recordings in many different species. Although these brain rhythms have been studied for many years, the methods for identifying discrete oscillatory epochs are still widely variable across studies. The "better oscillation detection" (BOSC) method applies standardized criteria to detect runs of "true" oscillatory activity and rejects transient events that do not reflect actual rhythms. It does so by estimating the background spectrum of the actual signal to derive detection criteria that include both power and duration thresholds. This method has not yet been applied to nonhuman data. Here, we test the BOSC method on two important rat hippocampal oscillatory signals, the theta rhythm and slow oscillation (SO), two large amplitude and mutually exclusive states. The BOSC method detected both the relatively sustained theta rhythm and the relatively transient SO apparent under urethane anesthesia and was relatively resilient to spectral features that changed across states, complementing previous findings for human EEG. Detection of oscillatory activity using the BOSC method (but not more traditional Fourier transform-based power analysis) corresponded well with human expert ratings. Moreover, for near-continuous theta, BOSC proved useful for detecting discrete disruptions that were associated with sudden and large amplitude phase shifts of the ongoing rhythm. Thus, the BOSC method accurately extracts oscillatory and nonoscillatory episodes from field potential recordings and produces systematic, objective, and consistent results-not only across frequencies, brain regions, tasks, and waking states, as shown previously, but also across species and for both sustained and transient rhythms. Thus, the BOSC method will facilitate more direct comparisons of oscillatory brain activity across all types of experimental paradigms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".