De-Noising of EEG Signals with Non-Physiological Noise
Bibliographic record
Abstract
Electroencephalography (EEG) signals are widely used in brain-computer interfaces, clinical diagnostics, and neuroscience topics. However, these signals are often contaminated by physiological and non-physiological artifacts, such as eyerelated artifacts and power line interference. While various denoising techniques have been developed, most focus on removing physiological artifacts, with limited attention given to random noise caused by equipment and ambient conditions. This type of non-physiological noise typically exhibits statistical properties that approximate a normal distribution (Gaussian distribution). In this study, we simulate such artifacts by adding Gaussian noise (amplified by a factor of 50) to clean EEG signals from the EEGdenoiseNet dataset. We evaluated 14 classical and modern denoising methods using signal-to-noise ratio (SNR), mean squared error (MSE), and normalized cross-correlation (NCC). Experimental results show that the regression method (RM), implemented as ridge regression, achieves the lowest MSE and highest NCC, outperforming deep learning-based approaches such as convolutional neural networks (CNN) under these conditions. These findings demonstrate the effectiveness of regression-based techniques in mitigating simulated non-physiological artifacts and provide guidance for future EEG signal processing. The codes are available at https://github.com/AIPMLab/EEG-Denoising.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".