Single-Channel EEG Artifact Identification with the Spectral Slope
Bibliographic record
Abstract
Abstract Electroencephalogram (EEG) signals are a valuable recording technique to diagnose neurological disorders and identify noninvasive biomarkers for clinical application, however, they are vulnerable to various artifacts. It is difficult to define exact parameters which efficiently distinguish artifacts from neural activity, and thus cleaning EEG data often relies on labor-intensive visual scoring methods. While signal processing techniques to remove artifacts exist, many state-of- the-art techniques are designed for multivariate signals, which can be challenging to implement in recording setups with few electrodes. We demonstrate how the spectral slope - a method previously used to distinguish between conscious states by linear regression of the logarithmic EEG power spectra - can also be used to identify epochs contaminated by recording artifacts in rat EEG recordings and propose this as a first pass artifact detection method. We computed the mean spectral slope for both ‘clean’ and ‘noisy’ epochs and compared the distributions among individual recordings to determine whether the decision threshold should be dynamic or fixed. We found no significant difference between the mean of these distributions and determined that a spectral slope threshold of -8 μV 2 /Hz was effective at identifying noisy epochs across all recordings. The accuracy of our method was evaluated against visually scored recordings and obtained an average accuracy, F1 and Cohen Kappa score of 94.2%, 86.4%, and 83%, respectively, across all epochs. Our study contributes to the automation of EEG artifact detection by presenting a straightforward initial method for identifying contaminated epochs based on the spectral slope of a single EEG channel in rodent recordings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".