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The new brain implant that turns from thought into text



Although texting is not as quick as talking. As it is a normal mode of communication. Earlier tests demonstrated that a paralyzed man implanted with a brain in his premotor cortex. It will be able to type 90 words per minute.

TRANSLATION OF BRAIN THOUGHTS TO TEXT
A brain implanted with a microchip enables a paralyzed man to communicate with their thoughts. Through text at speeds comparable to those of a regular smart phone user. This accomplishment represents the most recent advancement in "brain-computer interface" (BCI) systems.
For years, scientists have been researching BCI technology. In the hope of one day providing greater mobility to people with paralysis or limb amputations in their everyday lives.

HOW DOES IT WORK?
Tiny chips are inserted in regions of the brain associated with movement. Where they detect electrical activity in cells. When a person visualizes themselves performing a movement, the related brain cells fire. These electrical signals are then transmitted through wires to a computer. Where they are "decoded" and converted into motion. Consequently, enabling individuals to operate assistive devices using only their mental faculties.
BCI has been used by researchers at a few universities to help a small number of patients to mentally monitor robotic limbs or transfer computer cursors to "type" text.
With a speed of 90 characters per minute. And an accuracy of over 90% after autocorrect. The machine smashes any previous record set with a neural implant.
This might aid in the restoration of contact in seriously paralyzed or locked-in individuals. It should assist individuals in expressing themselves and sharing their opinions. It's very thrilling.

A SPARK OF GENIUS
A scientific research group collaborated with a volunteer. A 65-year-old man paralyzed from the neck down, to identify neural activity associated with handwriting. While the participant imagined writing letters, the task was fed into an algorithm, which converted it to text displayed on a computer in real time.
The device would allow paralyzed individuals to type and communicate effectively without their hands used. Similar brain implant technology was developed.
Mind-Texting Dominance
The 65-year-old participant (called T5 in the study) was able to type 90 characters per minute with an accuracy rate of 94.1 percent. That is roughly equivalent to texting on a smartphone. Perhaps more remarkable, the approach for visualizing letters significantly outperformed previous "virtual keyboard" efforts. This included implanting two brain chips into T5's premotor cortex, along with 200 electrodes. According to scientists, this district area of brain regulates movement intentions and thus makes a perfect candidate for the method of collecting handwriting input.
When asked to copy a sentence, T5 was able to "mind-text" at a rate of approximately 90 characters per minute, the team wrote, "the highest typing rate ever recorded for any form of BCI," and a twofold improvement over previous setups. His freestyle typing efficiency was comparable to that of his age group's average thumb texting speed.

THE PROBLEM? 
Controlling a cursor with your mind to touch letters on a digital keyboard is excruciatingly slow. The most effective implant so far operates at an average rate of 40 characters per minute and involves surgery and preparation. Even a non-invasive off-the-shelf eye-tracking keyboard will help people with paralysis type slightly faster.

CONCLUSION
Yes, tapping into machine learning algorithms is a good move, as the field is rapidly improving and demonstrating another strong link between neuroscience and machine learning. Perhaps more significantly, an algorithm's success is contingent upon the availability of high-quality data. The team discovered that the time difference between writing letters, which is very complicated, is what allowed the algorithm to perform so well in this case. In other words, it may be beneficial for future BCIs to decode complex behaviors rather than simple ones, especially for classification tasks.

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