ArticleFrontiers in neuroscience2018
Implicit Grasp Force Representation in Human Motor Cortical Recordings.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 27 citations in OpenAlex.
- Distinct Neural Modes Carry Information About Attempted Grasp Timing and Force in the Sensorimotor Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Multi-gesture drag-and-drop decoding in a 2D iBCI control task.Journal of neural engineering · 2025Article
- Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface.Journal of neural engineering · 2025Article
- Noninvasively recorded high-gamma signals improve synchrony of force feedback in a novel neurorehabilitation brain-machine interface for brain injury.Journal of neural engineering · 2022Article
- The neural mechanisms of manual dexterity.Nature reviews. Neuroscience · 2021Review
- Generalizable cursor click decoding using grasp-related neural transients.Journal of neural engineering · 2021Article
- Decoding spoken English from intracortical electrode arrays in dorsal precentral gyrus.Journal of neural engineering · 2020Article
- Neural Representation of Observed, Imagined, and Attempted Grasping Force in Motor Cortex of Individuals with Chronic Tetraplegia.Scientific reports · 2020Article
- Long-Term Home-Use of Sensory-Motor-Integrated Bidirectional Bionic Prosthetic Arms Promotes Functional, Perceptual, and Cognitive Changes.Frontiers in neuroscience · 2020Article
- Classifying Intracortical Brain-Machine Interface Signal Disruptions Based on System Performance and Applicable Compensatory Strategies: A Review.Frontiers in neurorobotics · 2020Review
- Article
- The Neural Representation of Force across Grasp Types in Motor Cortex of Humans with Tetraplegia.eNeuroArticle
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In order for brain-computer interface (BCI) systems to maximize functionality, users will need to be able to accurately modulate grasp force to avoid dropping heavy objects while also being able to handle fragile items. We present a case-study consisting of two experiments designed to identify whether intracortical recordings from the motor cortex of a person with tetraplegia could predict intended grasp force. In the first task, we were able classify neural responses to attempted grasps of four objects, each of which required similar grasp kinematics but different implicit grasp force targets, with 69% accuracy. In the second task, the subject attempted to move a virtual robotic arm in space to grasp a simple virtual object. For each trial, the subject was asked to grasp the virtual object with the force appropriate for one of the four objects from the first experiment, with the goal of measuring an implicit representation of grasp force. While the subject knew the grasp force during all phases of the trial, accurate classification was only achieved during active grasping, not while the hand moved to, transported, or released the object. In both tasks, misclassifications were most often to the object with an adjacent force requirement. In addition to the implications for understanding the representation of grasp force in motor cortex, these results are a first step toward creating intelligent algorithms to help BCI users grasp and manipulate a variety of objects that will be encountered in daily life.
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