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

To effortlessly complete an intentional movement, the brain needs feedback from the body regarding the movement's progress. This largely nonconscious kinesthetic sense helps the brain to learn relationships between motor commands and outcomes to correct movement errors. Prosthetic systems for restoring function have predominantly focused on controlling motorized joint movement. Without the kinesthetic sense, however, these devices do not become intuitively controllable. We report a method for endowing human amputees with a kinesthetic perception of dexterous robotic hands. Vibrating the muscles used for prosthetic control via a neural-machine interface produced the illusory perception of complex grip movements. Within minutes, three amputees integrated this kinesthetic feedback and improved movement control. Combining intent, kinesthesia, and vision instilled participants with a sense of agency over the robotic movements. This feedback approach for closed-loop control opens a pathway to seamless integration of minds and machines.

Illusory movement perception improves motor control for prosthetic hands.

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.

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.

Dylan T. Beckler

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

Dylan T. Beckler

.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