Accurate neural control of a hand prosthesis by posture-related activity in the primate grasping circuit
Bibliographic record
Abstract
Summary Brain-computer interfaces (BCIs) have the potential to restore hand movement for people with paralysis, but current devices still lack the fine control required to interact with objects of daily living. Following understanding of cortical activity during arm reaches, hand BCI studies have focused on velocity control. However, mounting evidence suggests that posture, and not velocity, dominates in hand-related areas during natural movement. To explore whether this signal can causally control a prosthesis, we developed a novel BCI training paradigm centered on the reproduction of hand posture transitions. Macaque monkeys trained with the protocol were able to control a multi-dimensional hand prosthesis at high-accuracy, including execution of the very intricate precision grip. Subsequent analysis revealed that the posture signal in the target grasping areas was a major contributor to control. Population activity exhibited pattern separation and dimensionality increases driven by posture kinematics, and simulations with a grasping circuit model demonstrated the generalizability of our approach. We present for the first time neural posture control of a multi-dimensional hand prosthesis, opening the door for future devices to leverage this additional information channel.
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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 | 0.000 |
| 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".