Measuring human locomotor control using EMG and EEG: Current knowledge, limitations and future considerations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Electrical signals encoding different forms of information can be observed at multiple levels of the human nervous system. Typically, these signals have been recorded in a rather isolated fashion with little overlap between the static recordings of electroencephalography (EEG) commonly used in neuroscience and the typical surface electromyography (EMG) recordings used in biomechanics. However, within the last decade, there has been an emerging need to link the electrical activation patterns of brain areas during movement to the behavior of the musculoskeletal system. This review discusses some of the most recent studies using the EEG and/or EMG to study the neural control of movement and human locomotion as well as studies quantifying the connectivity between brain and muscles. The focus is on rhythmic locomotor-type activities; however, results are discussed within the framework of initial work that has been done in upper and lower limbs during static and dynamic contractions. Limitations and current challenges as well as the possibility and functional interpretation of studying the connectivity between the cortex and skeletal muscles using a measure of coherence are discussed. The manuscript is geared toward scientists interested in the application of EEG in the field of locomotion, sports and exercise.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 it