BCI based on pedal end-effector triggered through pedaling imagery to promote excitability over the feet motor area
Bibliographic record
Abstract
According to the World Health Organization, stroke is the main cause of motor disability worldwide. After a stroke, many patients become dependent on other people to carry out activities of daily living. Thus, new rehabilitation technologies, such as brain-computer interfaces (BCIs), have been proposed to help or induce the reorganization of neural circuits. Furthermore, pedaling exercises have great potential for lower-limb recovery. This study analyzes through the electroencephalogram (EEG) of eight healthy subjects and two post-stroke patients, the cortical effect produced while each one commands through pedaling motor imagery (MI), a BCI to receive passive pedaling as feedback. EEG data were band-pass filtered, removing artifacts by applying Artifact Subspace Reconstruction-based Riemannian geometry, and after analyzed into the time-frequency representation and frequency domain. Significant event-related desynchronization (ERD) patterns focused around the foot motor area (Cz location) were obtained for low (13–22 Hz) and high (23–35 Hz) beta bands, during both imaginary and real motor tasks. As a result, ERD power decreasing was more emphasized at the instant that participants successfully triggered the BCI through MI and received as feedback passive movements. Also, we found on Cz a correlated cortical activity into the frequency domain, comparing periods of MI and passive movements. The findings suggest that low-cost BCIs based on pedal end-effector for lower-limb rehabilitation may be suitable to promote activations over the human primary motor cortex.
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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) | 0.003 | 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".