Scientists Created Neurochip That Turns Thoughts Into Words With Accuracy Of Over 90%
Date
8/29/2024 3:15:24 PM
(MENAFN- AzerNews)
By Alimat Aliyeva
Swiss scientists from the federal Polytechnic University of
Lausanne (EPFL) have developed a miniature brain-computer interface
that converts neural signals into text with high accuracy and low
power consumption, Azernews reports.
The chip uses a new approach based on the identification of
specific neural markers for each letter, which reduces the amount
of processed data to one hundred bytes per marker. This makes the
device more compact and less invasive compared to analogues such as
Neuralink.
The chip decodes brain activity and converts it into text with
91% accuracy.
The device, dubbed the "miniature brain-computer interface"
(MiBMI), is extremely small: it consists of two thin chips
measuring only 8 mm2. For comparison, Elon Musk's Neuralink
neuroimplant is much larger - about 23×8 mm. The EPFL chip consumes
little energy, is considered minimally invasive, and is a fully
integrated system that processes data in real time. Neuralink, in
turn, requires the installation of 64 electrodes in the brain and
processes the data through an application on an external
device.
Like other brain-computer interfaces, the new chip mainly tracks
the electrical activity of the brain and, based on data from
previous neurobiological studies, converts this activity into an
output signal. Specifically, MiBMI is able to read brain signals
that occur when a person imagines writing a letter, and output
these signals as text.
Part of the chip's success lies in a new approach to reading the
language processing signals that the brain sends. While working on
the chip, EPFL researchers discovered a number of very specific
neural markers that are activated when a person imagines the
spelling of each letter. These markers have been called
"distinctive neural codes" (DNC).
DNC became a kind of shorthand for each letter, which allowed
the MiBMI chipset to process exclusively the tokens themselves. The
amount of data associated with each marker is about one hundred
bytes, while the typical amount of neural data corresponding to the
representation of a letter is measured in thousands of bytes. This
reduction in the amount of processed data allowed to reduce the
size of the chip and reduce power consumption. The approach will
also reduce the time required to train users using the chip. MiBMI
is already capable of recognizing 31 characters, which is a record
for such integrated systems. The researchers plan to increase this
figure to 100 characters.
The EPFL chip is designed to help people who cannot speak or
move communicate with others. Researchers are now exploring
possible applications of the system beyond text processing,
including speech decoding and motion control. Their goal is to
create a universal neurochip that can be adapted for various
neurological disorders.
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