Biofeedback for Movement Disorders (Dystonia with Parkinson's Disease): Theory and Preliminary Results
Bibliographic record
Abstract
Background.This paper presents a theoretical framework for using a combination of EEG biofeedback plus regular biofeedback with clients who have movement disorders. Method.A case study is included that describes intervention and results with a 47-year-old woman with the dual diagnosis of Parkinson's disease and dystonia.The rational for adding biofeedback interventions to traditional medical treatment hinges on the fact that muscle spindles, which are involved in muscle movement and tone, have double innervations, cholinergic and sympathetic (Passatore, Grassi, & Filippi, 1985).Both of these systems can be operantly conditioned using biofeedback.There were two learning goals: (1) increase the production of 12 to 15 Hz activity since this sensor motor rhythm (SMR) is associated with decreased firing of the red nucleus and the red nucleus, in turn, has links to the muscle spindles (Sterman, 2000); (2) train for calm, relaxed autonomic nervous system functioning (decreased sympathetic drive and parasympathetic ascendance) because this may also have a beneficial effect on muscle tone by means of influencing muscle spindle activity (Banks, Jacobs, Gevirtz, & Hubbard, 1998).Training for balanced autonomic system functioning is facilitated by diaphragmatic breathing at a rate of about six breaths per minute.Diaphragmatic breathing results in respiration and heart rate variability, presented as a line graph, following
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".