Denoising fNIRS Signals to Enhance Brain Imaging Diagnosis
Bibliographic record
Abstract
Functional Near Infrared Spectroscopy (fNIRS) signals have the potential to permit accurate analysis of intracortical brain physiologic disease. The HomER graphical user interface is used to display the NIRS data, FastICA is updated to reduce data dimension and combined Wavelet & Pca method is developed to denoise NIRS signals. These signals include several types of noise spread from low to high frequencies such as respiratory interference frequency band of 0.1-0.3Hz, NIRS Mayer wave which is about 0.1Hz, cardiac interference frequency band which is 0.8-2.0Hz, artifacts from head and facial motions, and high frequency noise generated from electronic components. Wavelet & Pca is an efficient method to reduce biological noise, motion artifact and high-frequency noise. The applied processing technique consists of adaptively modifying the wavelet coefficients based on the degree of noise contaminating the processed NIRS signal. This is done subsequently to signal pre-processing by reducing data dimension using the FastICA method. The feasibility of the method was demonstrated by testing it on experimental fNIRS data collected from 47 subjects. Preliminary results, through signal-to-noise ratio and correlation indicators show that the technique reduces noise and improves the quality of the acquired NIRS signals and its corresponding analysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".