Leveraging Large Language Models for Automated Feature Extraction and Model Training in EMG-Based Motion Decoding
Bibliographic record
Abstract
Feature extraction and model training are critical steps in developing machine learning models for electromyography (EMG) based motion decoding. Traditionally, these processes require domain expertise and programming knowledge to implement signal processing algorithms with optimized model training pipelines. In this work, we investigate the feasibility of using Large Language Models (LLMs) to automate both the extraction of features from EMG data and the development of machine learning models for decoding human motion with minimal human intervention. More specifically, we compare LLM extracted features and their corresponding motion decoding models against those developed using manually developed code. Our results indicate that LLM extracted features and their corresponding trained models achieve performance comparable to traditional methods, demonstrating the potential of accelerating research and scientific investigations with AI-driven biosignal processing. This study highlights LLMs' capabilities and limitations in replacing manual coding for developing muscle-machine interfaces and provides insights into their integration into biomedical signal analysis workflows.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".