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AI-enabled MRI translates brain activity into text

AI-enabled MRI translates brain activity into text

Technology News |
By Nick Flaherty



Researchers in Texas are patenting an AI technique to use MRI imaging and other sensing mechanisms to turn brain activity into text.

The researchers at the University of Texas at Austin developed the AI system called a semantic decoder that is similar to the transformer model used by OpenAI’s GPT and Google’s Bard. This can translate a person’s brain activity — while listening to a story or silently imagining telling a story — into a continuous stream of text.

This is intended to help people who are mentally conscious yet unable to physically speak, such as those debilitated by strokes, to communicate intelligibly again.

Unlike other language decoding systems in development, this system does not require subjects to have surgical implants, making the process noninvasive. Participants also do not need to use only words from a prescribed list. Brain activity is measured using a functional MRI (fMRI) scanner which measures blood flow in the brain called the blood oxygenation level dependent (BOLD) contrast technique.

The system currently is not practical for use outside of the laboratory because of the reliance on the time need on an fMRI machine. But the researchers think this work could transfer to other, more portable brain-imaging systems, such as functional near-infrared spectroscopy (fNIRS).

After extensive training of the decoder, in which the individual listens to hours of podcasts in the scanner, the participant is open to having their thoughts decoded, their listening to a new story or imagining telling a story allows the machine to generate corresponding text from brain activity alone.

“For a noninvasive method, this is a real leap forward compared to what’s been done before, which is typically single words or short sentences,” said Alex Huth, an assistant professor of neuroscience and computer science at UT Austin. “We’re getting the model to decode continuous language for extended periods of time with complicated ideas.”

The result is not a word-for-word transcript. Instead, researchers designed it to capture the gist of what is being said or thought, albeit imperfectly. About half the time, when the decoder has been trained to monitor a participant’s brain activity, the machine produces text that closely (and sometimes precisely) matches the intended meanings of the original words.

For example, in experiments, a participant listening to a speaker say, “I don’t have my driver’s license yet” had their thoughts translated as, “She has not even started to learn to drive yet.” Listening to the words, “I didn’t know whether to scream, cry or run away. Instead, I said, ‘Leave me alone!’” was decoded as, “Started to scream and cry, and then she just said, ‘I told you to leave me alone.’”

Beginning with an earlier version of the paper that appeared as a preprint online, the researchers addressed questions about potential misuse of the technology. The paper describes how decoding worked only with cooperative participants who had participated willingly in training the decoder. Results for individuals on whom the decoder had not been trained were unintelligible, and if participants on whom the decoder had been trained later put up resistance — for example, by thinking other thoughts — results were similarly unusable.

“We take very seriously the concerns that it could be used for bad purposes and have worked to avoid that,” said researcher Jerry Tang. “We want to make sure people only use these types of technologies when they want to and that it helps them.”

In addition to having participants listen or think about stories, the researchers asked subjects to watch four short, silent videos while in the scanner. The semantic decoder was able to use their brain activity to accurately describe certain events from the videos.

“fNIRS measures where there’s more or less blood flow in the brain at different points in time, which, it turns out, is exactly the same kind of signal that fMRI is measuring,” Huth said. “So, our exact kind of approach should translate to fNIRS,” although, he noted, the resolution with fNIRS would be lower.

The paper in Nature is here.

www.utexas.edu

 

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