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Implicit Grasp Force Representation in Human Motor Cortical Recordings. | LitMetric

AI Article Synopsis

  • To optimize brain-computer interface (BCI) systems, users must learn to modulate grasp force to effectively handle both heavy and fragile items.
  • A study involving a person with tetraplegia tested how well intracortical recordings from their motor cortex could predict intended grasp force, achieving 69% accuracy in classifying neural responses.
  • Findings showed that accurate force classification occurred during active grasping, not while moving or releasing the object, highlighting the need for improved algorithms for BCI users to manage daily objects.

Article 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. NCT01894802 https://clinicaltrials.gov/ct2/show/NCT01894802.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6220062PMC
http://dx.doi.org/10.3389/fnins.2018.00801DOI Listing

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