Using neurofeedback from motor cortex to reduce tremor in essential tremor
Bibliographic record
Abstract
Elusive to a cause, Essential Tremor (ET) has been classified as the most common adult movement disorder. Current research suggests that ET may be centrally driven and stem from an oscillating neural network involving motor cortex and subcortical regions. ET is typically treated through costly, invasive methods to disrupt this oscillating network by targeting subcortical regions within the brain. Another, non-invasive, method which may disrupt this same network is neurofeedback (NF). Using real-time representation of activity in the brain, NF is a technique which encourages the user to self-manipulate their own brain activity. However, many people have difficulty controlling the NF signal. Current research lacks a determinant of who can and cannot use NF successfully. To that end, the purpose of this study is to determine whether individuals diagnosed with ET are capable of manipulating power of the theta and beta (4:8hz, and 12-15hz) frequency bands using NF. Participants were given audio feedback, through headphones, to train them to enhance and suppress a theta/beta ratio, in separate sessions, using EEG recorded over motor cortex. During each session participants underwent, eight, three-minute blocks of NF. Tremor was recorded pre and post training using accelerometers. Similar to previous work using other patient populations and healthy controls, there were individual differences in the ET patients who were able to control the NF signal. Exploring these individual differences provides a basis of knowledge for future NF studies to explore the impact of using longer training protocols to reduce tremor in patients with ET.Acknowledgments: Nova Scotia Health Research Foundation
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".