A Sensor-Fusion Method for Motion Artifacts Reduction in Intraoral EEG Signals
Bibliographic record
Abstract
In recent studies, electroencephalogram (EEG) signals are acquired intraorally from the palate region. However, intraoral EEG study is a less explored research area and its challenges are yet to be investigated. In this study, we look into the possibility of studying EEG signals from various intraoral locations and investigate the sources of motion artifacts during intraoral EEG measurements. Later, we propose a sensor fusion of EEG electrodes and accelerometer module to monitor intraoral EEG signal and intraoral motions simultaneously. The EEG electrodes, accelerometer, and sensor read-out circuitry are integrated with a mandibular advancement device (MAD). The system is battery-operated and uses a Bluetooth 5.0 transceiver to send data wirelessly. The smart MAD is used to acquire intraoral EEG and accelerometer data and a MATLAB-based algorithm is implemented using empirical mode decomposition (EMD) and independent component analysis (ICA) to decompose the EEG signal components. The decomposed ICA components containing intraoral motion artifacts are then mapped with the motion events extracted from the accelerometer data to identify the motion-corrupted data segments. The ICA components containing intraoral motions are then denoised by nullifying the motion-corrupted data segments. A motion artifact reduced intraoral EEG is reconstructed from the denoised ICA components. The efficacy of the sensor fusion and the proposed algorithm are demonstrated by quantifying the signal-to-noise ratio (SNR) difference and percentage artifacts reduction based on correlation analysis from the EEG signals before and after motion artifacts reduction. Later, the processed intraoral EEG signals are also analyzed for the detection of ‘eye open’ and ‘eye close’ activities in the presence of intraoral motions. The device along with the algorithm will have potential applications for motion artifact-free intraoral EEG monitoring.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".