characterization-sense-agency-over-actions-neural-machine-interface

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

This work describes a methodological framework that can be used to explicitly and implicitly characterize the sense of agency developed over the ...

Cleveland Clinic

Louis Stokes Cleveland Department of Veterans Affairs Medical Center

Fitts' law models the relationship between amplitude, precision, and speed of rapid movements. It is widely used to quantify performance in pointing tasks, study human-computer interaction, and generally to understand perceptual-motor information processes, including research to model performance in isometric force production tasks. Applying Fitts' law to an isometric grip force task would allow for quantifying grasp performance in rehabilitative medicine and may aid research on prosthetic control and design. We examined whether Fitts' law would hold when participants attempted to accurately produce their intended force output while grasping a manipulandum when presented with images of various everyday objects (we termed this the implicit task). Although our main interest was the implicit task, to benchmark it and establish validity, we examined performance against a more standard visual feedback condition via a digital force-feedback meter on a video monitor (explicit task). Next, we progressed from visual force feedback with force meter targets to the same targets without visual force feedback (operating largely on feedforward control with tactile feedback). This provided an opportunity to see if Fitts' law would hold without vision, and allowed us to progress toward the more naturalistic implicit task (which does not include visual feedback). Finally, we changed the nature of the targets from requiring explicit force values presented as arrows on a force-feedback meter (explicit targets) to the more naturalistic and intuitive target forces implied by images of objects (implicit targets). With visual force feedback the relation between task difficulty and the time to produce the target grip force was predicted by Fitts' law (average  = 0.82). Without vision, average grip force scaled accurately although force variability was insensitive to the target presented. In contrast, images of everyday objects generated more reliable grip forces without the visualized force meter. In sum, population means were well-described by Fitts' law for explicit targets with vision ( = 0.96) and implicit targets ( = 0.89), but not as well-described for explicit targets without vision ( = 0.54). Implicit targets should provide a realistic see-object-squeeze-object test using Fitts' law to quantify the relative speed-accuracy relationship of any given grasper.

Fitts' Law in the Control of Isometric Grip Force With Naturalistic Targets.

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.

Implicit Grasp Force Representation in Human Motor Cortical Recordings.

Indices of inter-evaluator reliability are used in many fields such as computational linguistics, psychology, and medical science; however, the interpretation of resulting values and determination of appropriate thresholds lack context and are often guided only by arbitrary "rules of thumb" or simply not addressed at all. Our goal for this work was to develop a method for determining the relationship between inter-evaluator agreement and error to facilitate meaningful interpretation of values, thresholds, and reliability.

Zachary C. Thumser

Laboratory for Bionic Integration,
Department of Biomedical Engineering,
Lerner Research Institute,
Laboratory for Bionic Integration, Department of Biomedical Engineering, Lerner Research Institute

Cleveland Clinic

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

Zachary C. Thumser

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Laboratory for Bionic Integration,Department of Biomedical Engineering,Lerner Research Institute,Laboratory for Bionic Integration, Department of Biomedical Engineering, Lerner Research Institute

Cleveland Clinic

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

Laboratory for Bionic Integration,
Department of Biomedical Engineering,
Lerner Research Institute,
Laboratory for Bionic Integration, Department of Biomedical Engineering, Lerner Research Institute