Efficacy of low-cost wireless neurofeedback to modulate brain activity during motor imagery
Bibliographic record
Abstract
OBJECTIVES: Motor imagery can be used as an adjunct to traditional stroke rehabilitation therapies for individuals who have hand and arm impairment resulting from their stroke. The provision of neurofeedback during motor imagery allows individuals to receive real time information regarding their motor imagery-related brain activity. However, the equipment required to administer this feedback is expensive and largely inaccessible to many of the individuals who could benefit from it. Available EEG-based technology provides an accessible, low-cost, wireless alternative to traditional neurofeedback methods, with the tradeoff of lower gain and channel count resulting in reduced signal quality. This study investigated the efficacy of this wireless technology for the provision of motor imagery-related neurofeedback. APPROACH: Twenty-eight healthy individuals participated in a 2-group, double-blinded study which involved imagining performing a unimanual button pressing task while receiving neurofeedback that is either a direct transform of their motor imagery-related brain activity (i.e., real) or is related to someone else's brain activity (i.e., sham). The change in amplitude of 15-30 Hz (beta) rhythmic brain activity elicited during the task blocks was calculated and analyzed across sessions and groups. MAIN RESULTS: We found that individuals who received real neurofeedback showed a statistically significant positive trajectory in modulating the amplitude of the beta rhythm across sessions, while those who received sham feedback showed a negative trajectory. Our results did not indicate a trend of increased lateralization across sessions, as has been shown in previous studies. SIGNIFICANCE: Our main findings replicated previous results with research-grade equipment indicating that there is potential for introducing this wireless technology for the provision of neurofeedback. Given the marginal longitudinal effect of neurofeedback in our study, further study is required to address the limitations associated with this technology before our protocol can be implemented in a clinical setting.
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) | 0.000 | 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) | 0.001 | 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".