With this advance, future neuroprostheses could restore fine motor abilities for individuals with paralysis or neurodegenerative diseases, transforming everyday functionality.
Virtual hand movements closely matched real hand postures in trained rhesus monkeys.
During this training phase, the monkeys performed the hand movements with their own hand while simultaneously seeing the corresponding movement of the virtual hand on the screen.
The activity of populations of neurons in the cortical brain areas that are specifically responsible for controlling hand movements was measured.
“Accurate neural control of a hand prothesis by posture-related activity in the primate grasping circuit” by Andres Agudelo-Toro et al.
A new method has been devised by scientists to enhance neuroprostheses, rendering them more accurate and useful for routine tasks. Researchers have demonstrated improved control of virtual hands in rhesus monkeys, closely resembling fine motor skills, by analyzing neural signals linked to hand postures.
The findings of this study point to hand postures as the key to improving prosthesis control and repairing damaged nerve connections during tasks like grasping, as opposed to movement speed. This advancement has the potential to transform everyday functionality by restoring fine motor abilities for people with neurodegenerative diseases or paralysis through future neuroprostheses.
Important Information:.
Neuroprosthesis control precision increased by concentrating on hand posture signals.
Trained rhesus monkeys’ virtual hand postures closely resembled their actual hand postures.
The results hold promise for improving fine motor abilities in upcoming hand prostheses.
From DPZ.
Our daily lives involve power and precise grips, whether it be for carrying shopping bags or threading a needle. When we become paralyzed or suffer from diseases like ALS that cause progressive muscle paralysis, we come to appreciate the importance and greatness of our hands.
For decades, researchers have been working on neuroprostheses to aid patients. These prosthetic limbs—hands, arms, or legs—might restore mobility to individuals with impairments.
Brain-computer interfaces, which translate brain signals into movements and decode them to control a prosthesis, are used to bridge damaged nerve connections.
But up until now, prostheses, especially hand prostheses, have not had the fine motor skills needed to be used in daily life.
As per Andres Agudelo-Toro, the first author of the study and scientist in the Neurobiology Laboratory at the German Primate Center, “the neural data read by the computer interface that controls a prosthesis primarily determines how well it works.”.
The signals that regulate a grasping movement’s velocity have been the subject of earlier research on hand and arm movements. We sought to determine whether hand posture-representing neural signals might be a more suitable form of control for neuroprostheses. “.
The study’s subjects were Macaca mulatta, or rhesus monkeys. They possess strong fine motor skills and a highly developed nervous and visual system, just like humans. They are therefore especially well-suited to study grasping motions.
The researchers trained two rhesus monkeys to move a virtual avatar hand on a screen in order to get them ready for the main experiment. The monkeys used their own hands to perform the hand movements during this training phase while also seeing the corresponding movement of the virtual hand on the screen. The monkeys’ hand movements were recorded while they performed the task thanks to a data glove equipped with magnetic sensors.
In a subsequent phase, the monkeys were taught to manipulate the virtual hand by “imagining” the grip after they had mastered the task. Measurements were made of the activity of populations of neurons in the cortical brain regions that are specifically in charge of regulating hand movements.
In order to translate neural data into movement, the brain-computer interface’s algorithm was modified in a protocol that corresponded to the signals that the researchers used to represent the various hand and finger postures.
We changed the algorithm to make it so that a movement’s destination and method of arrival are both significant, departing from the traditional protocol. e. Andres Agudelo-Toro clarifies, “the path of execution.”.
The most accurate outcomes were eventually obtained as a result. “.
After comparing the avatar hand’s movements with the real hand’s data from earlier recordings, the researchers were able to demonstrate that both were performed with a similar level of accuracy.
Head of the Neurobiology Laboratory and study senior author Hansjörg Scherberger says, “In our study, we were able to show that the signals that control the posture of a hand are particularly important for controlling a neuroprosthesis.”.
The functionality of upcoming brain-computer interfaces and, consequently, the fine motor skills of neural prostheses, can both be enhanced by these findings. “.
Funding: The European Union’s Horizon 2020 project B-CRATOS (GA 965044) and the German Research Foundation (DFG, grants FOR-1847 and SFB-889) provided funding for this study.
Concerning the news about neuroprosthetics and neurotech research.
Writer: Susanne Diederich.
From DPZ.
DPZ’s Susanne Diederich can be reached.
Picture: Neuroscience News is credited with this picture.
Original Study: Disclosed under open license.
“Accurate neural control of a hand prosthesis through posture-related activity in the grasping circuit of primates” Andres Agudelo-Toro et al. Neuron.
Abstraction.
Proper neural control of a hand prosthesis through posture-related activity in the grasping circuit of primates.
Though brain-computer interfaces (BCIs) have the potential to help paralyzed individuals move their hands again, the fine control that these devices require to interact with everyday objects is still lacking in current models.
Since we now know that cortical activity occurs during arm reaches, velocity control has been the main focus of hand BCI research. Nevertheless, an increasing body of research indicates that in hand-related domains, posture—rather than velocity—dominates.
We created a posture transition training paradigm for BCI in order to investigate whether this signal can be used to causally control a prosthesis. After undergoing training using this protocol, monkeys demonstrated high accuracy control over a multidimensional hand prosthesis, including the ability to perform the incredibly complex precision grip.
The primary source of control, according to analysis, was the posture signal in the target grasping regions. We demonstrate neural posture control of a multidimensional hand prosthesis for the first time, paving the way for future interfaces to take advantage of this extra information channel.
