An automatic method to generate ensemble averages of movement-related potentials for individuals with spinal cord injuries
Bibliographic record
Abstract
Ensemble averaging of the electroencephalogram is known to be a good tool for characterizing various event related potentials. An important part of ensemble averaging is to know the time reference that the signals should be averaged. In able-bodied individuals the muscle activity or switch activation is used to time-lock the averages. In people with spinal cord injuries who lack the ability to produce muscle activity, the expected time of the attempted movement based on an external cue can be used. This time is not accurate and can result in poor ensemble averages. A method that automatically detects the onset of the movement related potentials and use this knowledge to time-lock the averages is introduced. This method is based on the estimation of the probability density distribution of the feature vectors related to spontaneous EEG. To estimate the probability density function Parzen's method is used which is known to be as the most accurate method when large population of data is available. Preliminary experiments demonstrate the feasibility of the proposed method and show that the proposed method could generate ensemble averages closer to the averages with muscle activity knowledge than the method based on an external cue.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".