Electroencephalographic Determination of Movement Termination for Use in Stroke Neurorehabilitation
Bibliographic record
Abstract
Multiple changes occur in the brain of a stroke subject after the incident, many of these changes affect the control of muscle contraction and relaxation. This single-subject study to inspected cortical activity while a simple motor task was performed. We collected brain and muscle related electrical signals with an electroencephalographic electrode cap and Electromyographic electrodes in our lab (Robotics Lab at Shirley Ryan AbilityLab), with the goal of identifying altered brain correlates preceding and following grasp termination, as a part of the research and development of a novel device targeting neurorehabilitation. This study gave us the chance to test our hypothesis: differences in motor performance of the paretic hand with respect to the non paretic hand were related to different cortical activation patterns. Here, we measured motor related brain activation through the behaviour of sensorimotor rhythms of the motor cortex. Our experimental set-up demonstrated the feasibility to record, distinguish, and quantify brain modulations related to muscle activation and relaxation, imposing commands through visual stimuli to the subject. This study showed how markers of the brain dis-inhibition action on muscles can be detected in stroke patient with statistical significance (p < 0,005; bootstrap permutation test) in terms of Power Spectral Amplitude respect to baseline of Mu (8–13 Hz) and Beta (14–25 Hz) rhythms; moreover, we detected differences in activation patterns (mean Power Spectrum pre and post task) between the affected and non affected side. These findings provided us the opportunity to investigate the alterations of cortical activity in stroke patients, allowing to formulate further hypothesis on this phenomena. Moreover, this study on the mechanisms of movement termination will potentially help the research and development of a novel device targeting brain and muscle activation to control hand exoskeletons for stroke neurorehabilitation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| 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) | 0.008 | 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 teacher head, 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".