Denoising Near-Infrared Spectroscopy Signal
Bibliographic record
Abstract
Functional near-infrared spectroscopy (fNIRS) is a non-invasive method for detecting brain activity; however, noise, including motion artifacts and systemic interference, significantly impacts signal quality. To identify an effective denoising technique, we evaluated seven popular methods: Savitzky-Golay (SG) filter, spline interpolation, multivariate disturbance filtering(MDF), traditional band-pass filtering, coefficient of variation (CV) analysis, correlation-based signal improvement (CBSI) and time derivative distribution repair (TDDR), using a public dataset. Experimental results indicate that the SG filter offers the highest signal-to-noise ratio (SNR) of 27.65, and excels at removing high-frequency noise like spikes through averaging techniques. However, the CV method provides the highest contrast enhancement, with a contrast-to-noise ratio (CNR) of 19.41. Importantly, spline interpolation balances signal contrast and primary signal extraction effectively, so that we considered it as our best method. These findings offer valuable guidance for selecting appropriate filters in denoising fNIRS signals. The code and results are available on https://github.com/AIPMLab/Signal_SMC.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".