Brain implant helped paralyzed individuals type on a virtual keyboard - ForkLog: cryptocurrencies, AI, singularity, future

# Brain implant helps paralyzed people type on virtual keyboard

Two individuals with paralysis were able to type on a virtual keyboard thanks to an implant that decodes their finger movement attempts. One patient typed 80% faster than a healthy person, according to a study published in Nature Neuroscience.

Traditionally, brain-computer interfaces (BCI) for paralyzed individuals rely on eye-tracking or recognition of neural activity related to speech. However, researchers from Mass General Brigham and Brown University suggested that the familiar QWERTY keyboard format might be more convenient for many users.

“The most important thing is to have a variety of options for each patient to tailor the technology to their specific condition and situation,” said study author Justin Jude.

In the study, participants were asked to simulate typing on a QWERTY keyboard. The system reliably read brain impulses, recognizing up to 30 different actions—three for each of the ten fingers.

In testing the BCI device from Blackrock Neurotech, two people participated:

• Patient T17 (paralyzed below the neck due to spinal cord injury) reached a speed of 47 characters per minute with 81% accuracy;
• Patient T18 (suffering from amyotrophic lateral sclerosis, ALS) achieved 110 characters per minute with 95% accuracy.

The stability of the second person’s results was maintained for a week, while the first’s lasted two days.

Jude noted that the higher performance of one participant could be explained by the number and placement of electrodes in the brain. T18 has six arrays of contacts implanted in the dorsal (upper) part of the precentral gyrus—about three times more than T17.

The latter also had some electrodes placed in other areas of the motor cortex to collect speech signals.

Differences in results may also be explained by the fact that tetraplegia and ALS affect the brain differently, although both conditions lead to paralysis.

Jude emphasized that decoding finger movement signals could help restore complex hand movements in the future, including grasping and reaching for objects.

In the long term, precise recognition of finger motor skills could help patients regain control of prosthetics for complex manipulations like grabbing and reaching.

However, the technology still faces significant regulatory hurdles before it can become widely available to patients.

Recall that in March, China’s regulator approved the country’s first neuroimplant for commercial use.