Facial EMG contamination of EEG signals: Characteristics and effects of spatial filtering
Bibliographic record
Abstract
Facial electromyography (EMG) contamination of the electroencephalography (EEG) signals is a largely unresolved issue in brain-computer interface (BCI) research. Artifacts can obscure EEG features used in BCI control. Effective artifact detection and minimization require understanding of the artifacts. This study presents the spectral and the topographical properties of the artifacts caused by jaw clenching and eyebrow raising. Measures are introduced to quantify the effects of the artifacts on the EEG signals. We also compare the effectiveness of three spatial filtering methods (monopolar, small Laplacian and bipolar montage) in reducing the artifacts. Experiments on two subjects recorded the EEG signals during weak and moderate muscle contractions. The results show that the weak and the moderate contractions affect all frequencies at all locations (p ≪ 0.01). This clearly demonstrates that EMG artifact detection and minimization are important not only for the BCIs focused on mu and beta rhythms, but also other BCIs that involve low frequency components. ANOVA analysis reveals that the small Laplacian and the bipolar montage are susceptible to these artifacts. Caution has to be exercised when choosing a spatial filtering method as some may be effective in extracting features but do not perform as well in the presence of artifacts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".