Linear Discriminant Analysis with Bayesian Risk Parameters for Myoelectric Control
Bibliographic record
Abstract
The linear discriminant analysis (LDA) classifier remains a standard in myoelectric control due to its simplicity, ease of implementation, and robustness. Despite this, challenges associated with the temporal evolution of the myoelectric signal may require flexibility beyond the capabilities of standard LDA. Recently proposed approaches have leveraged more complex systems, such as adaptive window framing or temporal convolutional neural networks to incorporate temporal structure. In this work, we explore the potential of exploiting parameters inherent to the LDA, which is conventionally applied assuming static and equal prior probabilities and uniform cost functions, to improve myoelectric control. First, a cost-modified version of the LDA (cLDA) is introduced to better reflect the comparatively high cost of active errors. Second, an adaptive priors version of the LDA (pLDA) is introduced to reflect the changing prior probabilities of classes in the myoelectric signal time series. Results are compared against the standard LDA classifier using a novel dataset comprised of continuous class transitions. Although no significant differences were observed in total error, the proposed cLDA algorithm yielded significantly lower active error rates than the LDA alone. Furthermore, both the cLDA and pLDA classification schemes produced significantly lower instability than the LDA classifier alone, as measured by spurious changes in the output decision stream. This work lays the groundwork for future research on these flexible classification schemes including context dependent cost arrays, different methods of priors adaptation, and combinations of the proposed cLDA and pLDA frameworks.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".