Researchers achieve breakthrough in neural control of prosthetic hands

The novel method, tested on rhesus monkeys, demonstrated precise manipulation of prosthetic hands using neural signals alone. This study, published in Neuron, marks a shift in understanding the key neural components for controlling hand prostheses.

Traditionally, BCI research has focused on signals that govern the speed of movement. However, the German Primate Centre’s research found that the neural signals responsible for different hand postures play a more crucial role in controlling prosthetic devices. This insight is expected to enhance the fine control of neural prostheses, potentially restoring some or all mobility to individuals with conditions such as paraplegia or amyotrophic lateral sclerosis (ALS), where muscle paralysis limits the use of limbs.

The importance of fine motor control in everyday tasks is often underestimated until it is lost, with activities like carrying bags or threading a needle becoming challenging or impossible. Neuroprostheses, which use BCIs to bridge damaged nerve pathways and translate brain signals into movements, have been a promising solution for decades. However, the limited fine motor capabilities of current hand prostheses have hindered their practical use.

Andres Agudelo-Toro, a scientist in the Neurobiology Laboratory at the German Primate Centre and first author of the study, explained: “How well a prosthesis works depends primarily on the neural data read by the computer interface that controls it. Previous studies on arm and hand movements have focused on the signals that control the velocity of a grasping movement. We wanted to find out whether neural signals representing hand postures might be better suited to control neuroprostheses.”

The study involved two rhesus monkeys (Macaca mulatta), which share similarities with humans in nervous system development and fine motor skills, making them ideal for this type of research. Initially, the monkeys were trained to move a virtual avatar hand displayed on a screen using their own hand movements, recorded by a data glove equipped with magnetic sensors. In the subsequent phase, the monkeys learned to control the virtual hand through thought alone, with neuronal activity in the cortical brain areas that control hand movements being monitored.

To improve the algorithm that converts neural data into movement, the researchers adjusted the BCI protocol to prioritise not just the endpoint of a movement, but also the execution path. Agudelo-Toro noted: “Deviating from the classic protocol, we adapted the algorithm so that not only the destination of a movement is important, but also how you get there, i.e., the path of execution. This ultimately led to the most accurate results.”

Comparisons between the movements of the virtual hand and the real hand, as recorded previously, showed a high degree of precision, validating the effectiveness of the new protocol.

Hansjörg Scherberger, head of the Neurobiology Laboratory and senior author of the study, highlighted the implications of the findings: “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. These results can now be used to improve the functionality of future brain-computer interfaces and thus also to improve the fine motor skills of neural prostheses.”

The study was supported by the German Research Foundation through grants FOR-1847 and SFB-889, as well as the European Union's Horizon 2020 project B-CRATOS (GA 965044).

This breakthrough may pave the way for future developments in neuroprosthetic technology, offering new hope for individuals living with severe motor impairments.