SEPARATION AND IDENTIFICATION OF RHYTHM COMPONENTS OF LOCAL FIELD POTENTIAL SIGNALS IN AWAKE MICE USING ENSEMBLE EMPIRICAL MODE DECOMPOSITION
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Bibliographic record
Abstract
Decomposition of local field potential (LFP) signals with different oscillatory rhythms is useful for analysis of various neuronal activities in mice. In this paper, we first removed the power-line interference with high signal fidelity by using a notch filter with infinite impulse response. Next, we applied the ensemble empirical mode decomposition (EEMD) method to separate the LFP signal into low-frequency, Delta, Theta, Beta, Gamma, Ripple, and high-frequency oscillations, in the form of different intrinsic mode functions (IMFs). The LFP signal components with different frequency bands were identified and then reconstructed from the IMFs within the same frequency range by analyzing their power spectral ratios (PSRs). Then, normalized autocorrelation functions of the resting respiratory signal and the reconstructed Delta oscillations were computed to estimate the corresponding power spectral densities by means of the Fourier transform. The results of LFP signal decomposition and oscillatory rhythm reconstruction demonstrated the effectiveness of the EEMD and PSR analysis methods. The coherence analysis results indicate that the primary periodicity peak of the Delta LFP component is definitely linked to that of resting respiration in an awake mouse. Our major contribution is to establish a novel LFP signal separation and identification procedure by combining the EEMD method with appropriate parameter setting and the power spectral analysis approach.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it