On the Use of FOOOF for Electroencephalography Quality Measurement and Device Assessment
Bibliographic record
Abstract
Electroencephalography (EEG) signals capture the electrical activity of the brain and have traditionally been used in clinical settings. More recently, with the development of mobile, wearable EEG devices their use has been explored for other applications, including emotion recognition, quality of experience monitoring, or fatigue detection, just to name a few. EEG signals are known to have a 1/f-noise like structure and are very low amplitude, thus making them highly susceptible to artefacts, such as power line interference, muscle movement, and eye blinks. Moreover, prototyping and development of new devices to record EEG signals may introduce additional sources of artefacts generated by different instrumentation settings. As such, automated quantification of the quality of the EEG signals has become important and the focus of recent research. Here, we propose a new quality metric based on the 1/f-noise structure of the EEG signal. Experimental results show the proposed metric classifying clean versus noisy EEG segments in a subject-independent setting with an accuracy of 86.0% for the AF7 electrode location and 64.6% for AF8, two electrode locations known to be highly degraded by artefacts. Additionally, the proposed metrics are shown to generalize well to unseen electrode locations. For example, a quality model trained on AF7 noisy EEG data achieved an accuracy of 61.4% when tested on data collected from the AF8 location. EEG, quality, fooof, prototyping
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".