An IC-PLS Framework for Group Corticomuscular Coupling Analysis
Bibliographic record
Abstract
Corticomuscular coupling analysis, i.e., examining the relations between simultaneously recorded brain (e.g., electroencephalography--EEG) and muscle (e.g., electro-myography-EMG) signals, is a useful tool for understanding aspects of human motor control. Traditionally, the most popular method to assess corticomuscular coupling has been the pairwise magnitude-squared coherence (MSC) between EEG and concomitant EMG recordings. In this paper, we propose assessing corticomuscular coupling by combining partial least squares (PLS) and independent component analysis (ICA), which addresses many of the limitations of MSC, such as difficulty in robustly assessing group inference and relying on the biologically implausible assumption of pairwise interaction between brain and muscle recordings. In the proposed framework, response relevance and statistical independence are jointly incorporated into a multiobjective optimization function to meaningfully combine the goals of PLS and ICA under the same mathematical umbrella. Simulations, performed under realistic assumptions, illustrated the utility of such an approach. The method was extended to address intersubject variability to robustly discover common corticomuscular coupling patterns across subjects. We then applied the proposed framework to concurrent EEG and EMG data collected in a Parkinson's disease (PD) study. The results from applying the proposed technique revealed temporal components in the EEG and EMG that were significantly correlated with one another. In addition to the expected motor areas, the corresponding spatial activation patterns demonstrated enhanced occipital connectivity in PD subjects, consistent with previous studies suggesting that PD subjects rely excessively on visual information to counteract the deficiency in being able to generate internal commands from their affected basal ganglia.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".