Bibliographic record
Abstract
Electroencephalography (EEG) signals are a valuable source of information for investigating brain activity and different types of brain-related disease diagnoses. However, EEG signals are often contaminated by various kinds of noises/artifacts. Several methods have been proposed for EEG reconstruction/denoising to facilitate signal analysis, but such algorithms often fail when the EEG contains extreme artifacts. This paper presents a novel method for reconstructing EEG signals using a variant of the variational autoencoder (VAE) called beta-VAE. Through extensive evaluation of our model on the DEAP dataset, we show that the β-VAE architecture learns a compressed representation of the EEG signal in an unsupervised manner, and the reconstructed signal contains less artifact. We compare our proposed method with different baselines and state-of-the-art techniques for EEG signal denoising, demonstrating significantly reduced reconstruction error under artificially induced noise. The results suggest that our approach has great potential for improving the analysis and understanding of EEG signals in clinical and research settings.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".