A team of researchers has managed to achieve a major milestone by reducing the energy requirements for neural implants while also improving overall accuracy. The discovery could pave the way for the development of advanced brain implants that could be employed to treat neurological diseases and facilitate the use of prosthetics.
By optimizing the use of a specific subset of brain waves, the team managed to reduce the amount of power consumed by neural interfaces by up to 90%.
Smaller and better
At this point, the conversion of brain signals into usable intentions relies on the use of complex computers and a significant amount of electrical power. By reducing the need for large and, power-hungry hardware scientists could create brain-machine interfaces that could be used in a variety of contexts, including at home.
Neurons, who are responsible for conveying essential information and signals around the body, generate a lot of noise. This means that the tools which are used to gather and collect neuronal data have to listen very closely to what is being collected so they can make the distinction between relevant information and the background noise.
For now, the direct conversion of complex gestures like grabbing an object relies on the use of transcutaneous electors, which are inserted surgically into the skin and the brain. While the approach allows the restoration of movement to a paralyzed arm or some feelings via prosthetic hands, it is quite impractical outside of laboratory conditions.
The SPB technique, which involves the compression of the relevant signals, limits the amount of data that needs to be filtered and processed while also retaining what is need to convey the desired messages. It is also worth noting that this method only needs 2,000 signals instead of the 20,000, which are mandatory for transcutaneous electrodes.
More information can be found in a paper published in a scientific journal.