A study by researchers at the University of Texas at Austin has developed a new type of brain-computer interface (BCI) that simplifies the use of these devices. This new technology allows users to control digital interfaces such as video games directly in their minds, without the time-consuming setup that was previously required for each individual. This 'one size fits all' solution has the potential to significantly improve the quality of life for people with movement disorders by making technology more accessible and easier to use.
This research PNAS Nexus.
BCIs that allow users to control devices using brain signals have traditionally required each new user to undergo an adjustment process to adjust the system to their unique neural patterns. This calibration is not only labor-intensive but also requires the expertise of trained personnel, making the use of technology particularly difficult in clinical settings where rapid adaptation from one patient to another is important. Ease of use and extensibility are limited.
The motivation for this study was to improve the practicality and ease of use of BCIs and make them more accessible to people with movement disorders. The researchers aimed to develop a system that could be adapted to any user with minimal initial setup, thereby democratizing the use of such advanced technology. By simplifying the calibration process, this technology can be used more efficiently in a variety of environments, from medical facilities to home healthcare, and even consumer electronics for gaming and interaction. Masu.
The University of Texas team, led by professor Jose del R. Millan, used a cap fitted with electrodes that measure electrical signals in the brain. These signals are decoded using machine learning algorithms and converted into commands for digital tasks.
To test the adaptability and effectiveness of the new system, the researchers selected a sample of 18 healthy volunteers without movement disorders. These subjects were given the challenge of controlling two different digital interfaces: a simple bar balancing game and a more complex car racing game.
The bar game was designed to be simple, requiring subjects to balance the left and right sides of a digital bar, and served as an initial training tool for the decoder. A more challenging car racing game required subjects to perform a series of turns, mimicking the planning and execution required in real-world driving scenarios.
What was unique about their experimental setup was that they used a machine learning model that could quickly adapt to each user's brain activity. The model was initially trained using data from her single expert user and then tuned to new users without the need for separate tuning sessions. The system employs advanced machine learning algorithms to interpret EEG signals and refine predictions through continuous user interaction, effectively learning in real-time.
The researchers found that the system successfully translated brainwave data into precise commands for both bars and racing games, allowing participants to effectively control the interface after minimizing exposure to the system. I discovered. This rapid adaptability was a significant advance over previous BCIs that required lengthy and complex adjustments for each new user.
“If you think about this in a clinical setting, this technology eliminates the need for a specialized team to go through this long and tedious calibration process,” said Satyam Kumar, a graduate student in Jose's lab. Ta. Del R. Millan is a professor in the Chandra Family Department of Electrical and Computer Engineering at the Cockrell School of Engineering and the Department of Neurology at the Dell Medical School. “It will move from patient to patient much faster.”
Additionally, this study demonstrated that machine learning models can not only adapt quickly, but also improve in accuracy and responsiveness as they are used. This improvement suggests that the system is continually learning and adjusting to the user's specific brain patterns, thereby increasing its effectiveness over time.
BCI's ability to operate efficiently across different tasks and users without individual readjustment makes it a new addition to its application, especially in environments where quick setup and ease of use are important, such as clinical settings and home care. new possibilities open up. hindrance.
Although the study results are promising, the technology was only tested on subjects without movement disorders. Future research should include people with disabilities to fully understand the effectiveness and practicality of the interface in the target user population. Furthermore, the tasks currently being tested are relatively simple. Future research is expected to examine more complex applications and long-term use to determine the extent to which the interface supports continuous learning and adaptation.
Additionally, this study highlights the potential to use this technology in more dynamic environments, such as controlling wheelchairs and other assistive devices, which could improve mobility and independence for people with severe mobility impairments. performance can be significantly improved.
“The purpose of this technology is to help people and help them with their daily lives,” Milan said. “We will continue on this path as far as we go in our pursuit of helping people.”
The study, “Transfer learning accelerates individuals' acquisition of BCI skills,” was authored by Satyam Kumar, Hussein Alawieh, Frigyes Samuel Racz, Rawan Fakhreddine, and José del R Millán.
