Groundbreaking implant wirelessly relays brain signals in high fidelity
Machines that connect to the human brain to gather and interpret its electrical signals have wide-ranging potential, from enabling paralyzed people control over robotic prostheses to supplementing human intelligence so it keeps pace with artificial intelligence. The team behind a project known as BrainGate have now demonstrated a wireless version of brain-computer interface (BCI) technology that can read and transmit neural signals at a bandwidth that is on par with wired systems, opening up exciting new possibilities in neuroscience research as well as patient care.
The idea behind BCIs is to monitor the electrical activity taking place in the brain and decode how that relates to the user's thoughts and intentions. By recognizing that a certain pattern of brain activity correlates with a desire to raise a right hand in a paralyzed person, for example, the brain-computer interface can turn that into a command for a prosthetic arm that then executes the movement.
And we've seen many exciting advances with these kinds of systems of late. They have allowed for exoskeletons to be controlled via thoughts to kick soccer balls, allowed quadriplegic women to drink coffee, and even allowed paraplegics to experience a sense of touch.
The team behind BrainGate, which is a consortium of scientists from a number of US universities and institutions, has been at the vanguard of this technology for decades. One major roadblock, for this team and all others in the field, is that for brain-computer interfaces to be able to gather truly meaningful amounts of data in real-time, they need to be implanted in the brain and tethered to computer systems that decode the signals, rather than monitor activity non-invasively through layers of skull and tissue, like an EEG cap.
This is the motivation behind Elon Musk's Neuralink startup, which aims to develop a brain-computer interface that can do the job wirelessly and discreetly, and just such an approach is indeed a widely-held ambition for researchers in the area. The BrainGate team now claims to have achieved this with its latest system, which it says is the first device to transmit the full spectrum of signals recorded by an intracortical sensor.
The system consists of an array of 200 electrodes that is implanted into the brain's motor cortex, and relays neural signals at 48 megabits per second to a connected wireless transmitter that sits atop the user's head. The system has a battery life of 36 hours and was demonstrated with the help of two paralyzed participants, who were able to use it continuously for up to 24 hours to point, click and type on a tablet computer.
“We’ve demonstrated that this wireless system is functionally equivalent to the wired systems that have been the gold standard in BCI (brain-computer interface) performance for years,” says John Simeral, an assistant professor of engineering at Brown University and the study’s lead author. “The signals are recorded and transmitted with appropriately similar fidelity, which means we can use the same decoding algorithms we used with wired equipment. The only difference is that people no longer need to be physically tethered to our equipment, which opens up new possibilities in terms of how the system can be used.”
While ultimately improving the quality of life for sufferers of paralysis is one key outcome of the breakthrough, this new device will also serve as a powerful tool for neuroscientists seeking to better understand brain activity, which will lead to even more advanced brain-computer interfaces down the track.
“We want to understand how neural signals evolve over time,” says Leigh Hochberg, an engineering professor at Brown University. “With this system, we’re able to look at brain activity, at home, over long periods in a way that was nearly impossible before. This will help us to design decoding algorithms that provide for the seamless, intuitive, reliable restoration of communication and mobility for people with paralysis.”
A paper describing the research was published in the journal IEEE Transactions on Biomedical Engineering.
Source: Brown University