A Combined Independent Component Analysis (ICA)/ Empirical Mode Decomposition (EMD) Method to Infer Corticomuscular Coupling
Bibliographic record
Abstract
EEG-EMG coherence has been recently used to investigate the motor system in humans. Typically this is performed by calculating the coherence between a single EEG electrode and a rectified EMG channel. However, there are strong biological reasons to expect that the cortical to muscular communication is many-to-many as opposed to one-to-one. Here we describe the use of independent component analysis (ICA) to find linear combinations of EEG channels and EMG channels separately. Empirical mode decomposition (EMD) is then used to determine intrinsic mode functions (IMFs) that estimated the envelope of the EMG ICs. We demonstrate that at least 2 EEG ICs correspond with EMG IC IMFs with much greater significance that the pairwise EEG-EMG comparison. Moreover, the proposed method successfully untangles the ~10 Hz and ~30 Hz effects of the corticomuscular coupling which are thought to underlie different neural processes. We suggest that the ICA/EMD approach is worthy of further exploration
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".