Scientists develop an AI system that translates thoughts into text


If you haven’t heard of locked-in syndrome, it is a rare neurological disorder where the patient is awake and conscious but “locked-in” their body, due to complete paralysis of all voluntary muscles in the body, but with a normal exception being their eyes.

In a bid to aid communication for patients who are unable to speak or type, such as those with locked-in syndrome, scientists have recently developed a system that can turn brain activity directly into a text — an important stepping stone towards “speech prosthesis” technology.

To test their system, the researchers recruited four participants who had electrode arrays implanted in their brains for monitoring purposes. They asked the participants to repeatedly read sentences aloud, while collecting neural information — a dataset they then used to train an algorithm to interpret the signals even when the subjects weren’t reading out loud.

While it still has its weaknesses, the accuracy of the system was far higher than previous approaches, with an error rate of just 3% for each sentence, compared to that of 5% for professional human transcribers.

And despite it now is just a prototype, the system represents a breakthrough in communication technology that could one day help ease the lives of people who are unable to type or speak.

Solution News Source

Scientists develop an AI system that translates thoughts into text


If you haven’t heard of locked-in syndrome, it is a rare neurological disorder where the patient is awake and conscious but “locked-in” their body, due to complete paralysis of all voluntary muscles in the body, but with a normal exception being their eyes.

In a bid to aid communication for patients who are unable to speak or type, such as those with locked-in syndrome, scientists have recently developed a system that can turn brain activity directly into a text — an important stepping stone towards “speech prosthesis” technology.

To test their system, the researchers recruited four participants who had electrode arrays implanted in their brains for monitoring purposes. They asked the participants to repeatedly read sentences aloud, while collecting neural information — a dataset they then used to train an algorithm to interpret the signals even when the subjects weren’t reading out loud.

While it still has its weaknesses, the accuracy of the system was far higher than previous approaches, with an error rate of just 3% for each sentence, compared to that of 5% for professional human transcribers.

And despite it now is just a prototype, the system represents a breakthrough in communication technology that could one day help ease the lives of people who are unable to type or speak.

Solution News Source

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