An Intra-oral EEG System with Accelerometer For Motion Artifact Free EEG Recording
Bibliographic record
Abstract
Electroencephalography (EEG) has been reported to be acquired intra-orally from the palate region. Although different types of motions such as gulping, tongue movements, teeth grinding can occur intra-orally, the motion artifacts in intra-oral EEG signals have not been studied. In this work, we propose a sensor-fusion of EEG electrodes and an accelerometer integrated with a mandibular device and demonstrate an algorithm to generate motion artifacts free intra-oral EEG signal by removing the motion corrupted EEG segments. The EEG electrodes and the accelerometer simultaneously record intra-oral EEG signals and motion signals, respectively. The signal recording instrument is implemented on a breadboard for this study. The algorithm implemented in MATLAB identifies the motion events from the accelerometer data, removes the motion corrupted EEG data segments using the accelerometer information, and finally reconstructs the EEG signals after the removal of the corrupted EEG segments. The effectiveness of the sensor-fusion and algorithm is demonstrated for the detection of ‘eye open’ and ‘eye close’ activity from intra-oral EEG signals in the presence of tongue movements, teeth grinding and gulping. The device along with the algorithm will have potential for motion artifacts free intra-oral EEG signal monitoring.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".