Elimination of Ocular Artifacts from EEG signals using the wavelet transform and empirical mode decomposition
Bibliographic record
Abstract
Electroencephalogram (EEG) is a biological signal that represents the electrical activity of the brain. EEG signal may be highly distorted by the eye-blinks and movements of the eyeballs that are collectively known as ocular artifacts (OA). OA severely limit the utility of the recorded EEG and thus need to be removed for better clinical evaluation. The frequency range of EEG signal is 0 to 64 Hz and the OA occur within the range of 0 to 16 Hz. Therefore, simple filtering techniques cannot be used to eliminate OA from EEG. This paper present a method based on the wavelet transform to automatically identify the OA zones in contaminated EEG signal. Then, only removing it's to obtain the clean EEG by the recently developed empirical mode decomposition (EMD) algorithm. Simulation results show the superiority of the proposed method in identification and removal of ocular artifacts from EEG signal comparison with other filtering approaches.
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 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".