ReMAE: User-Friendly Toolbox for Removing Muscle Artifacts From EEG
Bibliographic record
Abstract
This paper describes a user-friendly toolbox, ReMAE, for removing muscle artifacts from electroencephalogram (EEG), running under the MATLAB environment. It implements a series of state-of-the-art methods for muscle artifact removal from EEG in the literature, and provides a graphical user interface (GUI). According to the taxonomy of the existing studies, this toolbox contains three denoising modes based on the number of input EEG channels, i.e., multi-channel, single-channel, and few-channel. Furthermore, this toolbox modularizes the denoising methods and visualizes each module. This means that users can readily observe the detailed denoising performance in each step, and even design a customized combined method in terms of their own understanding. In the current literature, there exists no method applicable for all situations due to the complexity of muscle artifacts. The main motivation of this work is to connect neuroscientists, psychologists, and clinicians with both the well-established and cutting-edge methods through a simple and intuitive GUI, and encourage them to extensively investigate different methods in a variety of real scenarios.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.065 | 0.035 |
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".