UT Austin Develops Universal Brain-Computer Interface for Future Applications

Forget about joystick controllers! Imagine steering your car in a racing game, like Mario Kart, simply by thinking about the turns. This isn't a futuristic dream – it's a reality thanks to researchers at the University of Texas at Austin.

Their breakthrough lies in a new brain-computer interface (BCI) that doesn't require individual calibration. This has been a major obstacle for wider use of BCIs, as every brain is unique. The new system, however, can learn a user's patterns quickly through repetition, making it a potential "one-size-fits-all" solution.

Faster Set-up for More People

Previously, lengthy calibration sessions were needed for each user. This new self-calibrating BCI eliminates this hurdle. "We won't need a specialized team for calibration anymore," explains Satyam Kumar, a researcher on the project. This means faster and easier use for patients in clinical settings.

The research detailing this innovation appears in the journal PNAS Nexus.

From left to right: Satyam Kumar, Hussein Alawieh, and José del R. Millán. Credit: The University of Texas at Austin

The BCI uses a cap with electrodes that captures brainwave signals. These signals are then interpreted by a "decoder" and translated into actions within the game. This allows users to control the race car and even simpler tasks like balancing a digital bar on the screen.

The key to avoiding individual calibration is a "base decoder" trained by an expert on the simpler bar task. This decoder serves as a foundation for other users, allowing them to control the car racing game, which requires more complex thought patterns for maneuvering turns.

The Future of Brain-Computer Interfaces

The researchers consider this work a stepping stone for further advancements in BCIs. While this study involved individuals without motor impairments, future tests will target people with motor disabilities, paving the way for real-world applications.

Professor Millán, who leads the research team, emphasizes the dual purpose: "We aim to translate BCIs to the clinical world to empower people with disabilities, while also refining the technology for a more significant impact on their lives."

Beyond the racing game, Millán's team is exploring other possibilities. They're developing a brain-controlled wheelchair and have demonstrated the BCI's ability to operate rehabilitation robots for the hand and arm.

"Our goal is to create technology that assists people in their daily lives," says Millán. "We'll keep pushing forward to make a positive difference."


Published 16 February 2024 in PNAS Nexus; Transfer learning promotes acquisition of individual BCI skills 



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