Brain-to-Text Communication via Handwriting

We wrote about Brain Computer Interface (BCI) and Internet of Senses (IoS) back in August. It most likely touched a nerve as quite a few people discussed the topic with me in these last few months. I guess their main point was, is this related to 6G? I agree.  

In my 6G Training course, when I look at the technology part, I clearly mention that there are three things that most people are confusing. The devices, the connectivity (Access Network) and the Core Network. This is more on the device side where the evolution is independent of 6G and will happen regardless of how the connectivity evolution progresses.

Ericsson's vision of Internet of Senses (IoS) as can be seen in the video in the Tweet above is a good motivation of why we will lump this as part of 6G for the foreseeable future.

In an article published in the Nature Journal, researchers from Stanford University provided a rather impressive example of the promise of neural implants. Using an implant, a paralyzed individual managed to type out roughly 90 characters per minute simply by imagining that he was writing those characters out by hand.

The article requires subscription but here is a summary from ArsTechnica

With the implants in the right place, the researchers asked the participant to imagine writing letters on a page and recorded the neural activity as he did so.

Altogether, there were roughly 200 electrodes in the participant's premotor cortex. Not all of them were informative for letter-writing. But for those that were, the authors performed a principal component analysis, which identified the features of the neural recordings that differed the most when various letters were imagined. Converting these recordings into a two-dimensional plot, it was obvious that the activity seen when writing a single character always clustered together. And physically similar characters—p and b, for example, or h, n, and r—formed clusters near each other.

(The researchers also asked the participant to do punctuation marks like a comma and question mark and used a > to indicate a space and a tilde for a period.)

Overall, the researchers found they could decipher the appropriate character with an accuracy of a bit over 94 percent, but the system required a relatively slow analysis after the neural data was recorded. To get things working in real time, the researchers trained a recurrent neural network to estimate the probability of a signal corresponding to each letter.

Despite working with a relatively small amount of data (only 242 sentences' worth of characters), the system worked remarkably well. The lag between the thought and a character appearing on screen was only about half a second, and the participant was able to produce about 90 characters per minute, easily topping the previous record for implant-driven typing, which was about 25 characters per minute. The raw error rate was only about 5 percent, and applying a system like a typing autocorrect could drop the error rate down to only 1 percent.

6G visionaries see that if this succeeds, you can also embed connectivity and this could be a first step in Telepathy, where people can directly communicate with others without the need of any other device. No need to worry as this is definitely a long way away.

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