Signal Decomposition Method with Sensor-Fusion for Reducing Motion Artifacts in Intra-Oral EEG
Bibliographic record
Abstract
Electroencephalogram (EEG) signals have been successfully acquired intra-orally and reported in recent research works. However, most of the reported work did not investigate the challenges of studying intra-oral EEG signals such as intra-oral motions due to tongue movements, teeth grinding, gulping etc. In this work, we use a smart mandibular advancement device (MAD) integrated with a sensor-fusion of single EEG channel and accelerometer for simultaneous measurement of intra-oral EEG and motion data. A novel MATLAB based algorithm is proposed to decompose the intra-oral EEG signals using discrete wavelet transform (DWT) and independent component analysis (ICA). Then, the motion corrupted segments, present in the independent components, are selectively located with the help of the accelerometer motion data and denoised accordingly. A motion artifacts reduced intra-oral EEG signal is reconstructed from the modified independent components using inverse ICA-DWT method. Performance of the proposed algorithm is also validated quantitatively. The smart MAD along with the proposed algorithm will have potential for motion artifacts reduced intra-oral EEG measurements.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".