Brain Activity Decoder Can Reveal Stories in People’s Minds

AUSTIN, Texas — A new artificial intelligence system called a semantic decoder can translate a person's brain activity — while listening to a story or silently imagining telling a story — into a continuous stream of text. The system developed by researchers at The University of Texas at Austin might help people who are mentally conscious yet unable to physically speak, such as those debilitated by strokes, to communicate intelligibly again.

The study, published in the journal Nature Neuroscience, was led by Jerry Tang, a doctoral student in computer science, and Alex Huth, an assistant professor of neuroscience and computer science at UT Austin. The work relies in part on a transformer model, similar to the ones that power Open AI's ChatGPT and Google's Bard.

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 an fMRI scanner after extensive training of the decoder, in which the individual listens to hours of podcasts in the scanner. Later, provided that 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," Huth said. "We’re getting the model to decode continuous language for extended periods of time with complicated ideas."

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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," Tang said. "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.

The system currently is not practical for use outside of the laboratory because of its 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).

"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.

This work was supported by the Whitehall Foundation, the Alfred P. Sloan Foundation and the Burroughs Wellcome Fund.

The study's other co-authors are Amanda LeBel, a former research assistant in the Huth lab, and Shailee Jain, a computer science graduate student at UT Austin.

Alexander Huth and Jerry Tang have filed a PCT patent application related to this work.

For more information about the project, visit the full press release at the College of Natural Sciences.

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