Scientists have wirelessly connected a human brain to a computer for the first time in history.
The technological advance comes months after Elon Musk unveiled a working prototype of his Neuralink brain chip; only instead of a person, it was implanted in a pig named Gertrude.
Here, the whizzes at Brown University in Rhode Island have established a connection between a human brain and a computer, capable of transmitting signals with ‘single-neuron resolution and in full broadband fidelity.’
Amid emerging interest in brain-computer interfaces (BCI), the study saw two clinical trial participants with paralysis (two men aged 35 and 63, who’d earlier endured spinal chord injuries) using the BrainGate system with a wireless transmitter to point, click and type on a standard tablet computer, as per a press release.
Published in the IEEE Transactions on Biomedical Engineering journal, the system works using a small transmitter weighing just more than 40g. Instead of cables, a unit is placed on top of a user’s head where it ‘connects to an electrode array within the brain’s motor cortex using the same port used by wired systems.’
Incredibly, the participants managed to achieve the same level of accuracy and typing speed using the BrainGate tech, said to capable of using the BCI for up to 24 hours – even while they were sleeping, so the researchers could continue probing the data.
John Simeral, an assistant professor of engineering at Brown University and lead author on the study, said: ‘The signals are recorded and transmitted with appropriately similar fidelity, which means we can use the same decoding algorithms we used with wired equipment.’
He added: ‘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.’
Leigh Hochberg, an engineering professor at the university and leader of the trial, said: ‘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.’
