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Alex Huth

AI Brain Decoder Moves Closer to Real-World Use for People With Aphasia

Brain activity like this, measured in an fMRI machine, can be used to train a brain decoder to decipher what a person is thinking about. In this latest study, UT Austin researchers have developed a method to adapt their brain decoder to new users far faster than the original training, even when the user has difficulty comprehending language.

02/07/2025 - UT Austin researchers have improved their AI-powered brain decoder, allowing it to translate thoughts into continuous text with just one hour of training—far less than the original 16-hour process. This advancement makes the technology more accessible, particularly for individuals with aphasia, by enabling communication without requiring spoken language comprehension. The team is now collaborating with experts in aphasia research to explore its potential for real-world applications.

Brain Activity Decoder Can Reveal Stories in People’s Minds

Alex Huth (left), Shailee Jain (center) and Jerry Tang (right) prepare to collect brain activity data in the Biomedical Imaging Center at The University of Texas at Austin. The researchers trained their semantic decoder on dozens of hours of brain activity data from participants, collected in an fMRI scanner. Photo Credit: Nolan Zunk/University of Texas at Austin.

05/01/2023 - The work relies in part on a transformer model, similar to the ones that power ChatGPTA 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.

New Faculty 2016-17

09/19/2016 - 2016-17 marks the beginning of another outstanding year for UT Computer Science, with the addition of six new faculty in the fields of quantum computing, computer vision, natural language processing, and theory. This builds upon the very successful 2015-16 academic year, when UT Computer Science recruited four new assistant professors in systems and robotics, ensuring a vibrant future for computer science education and research at The University of Texas at Austin.

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