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

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.

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.

Article	Year
Illusory movement perception improves motor control for prosthetic hands. Science translational medicine\| PubMed ID: 29540617	2018
Implicit Grasp Force Representation in Human Motor Cortical Recordings. Frontiers in neuroscience\| PubMed ID: 30429772	2018
Reliability in evaluator-based tests: using simulation-constructed models to determine contextually relevant agreement thresholds. BMC medical research methodology\| PubMed ID: 30453897	2018

Paul D. Marasco

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