Hussein Alawieh, a graduate student in José del R. Millán's lab, wears a cap packed with electrodes that is hooked up to a computer. The electrodes gather data by measuring electrical signals from the brain, and the decoder interprets that information and translates it into game action. Credit: The University of Texas at Austin
Key points:
- Brain-computer interfaces help users guide and strengthen neural plasticity and can be used to improve the lives of people with motor disabilities.
- Researchers created a universal brain-computer interface that they trained with both a simple balancing task and a complex car racing game.
- This study is a first step for brain-computer interface innovation and brings the technology closer to helping people with motor disabilities in the clinical setting.
Engineers are developing brain-computer interfaces to help improve the lives of people with motor disabilities. In a new study, published in PNAS Nexus, they show how to integrate machine learning capabilities with a brain-computer interface to make a one-size-fits-all solution.
Brain-computer interfaces help users to guide and strengthen their neural plasticity—the brain’s ability to change, grow, and re-organize over time. In the current study, 18 subjects with no motor impairments trained an interface through a car racing game and simple task balancing the left and right sides of a digital bar.
An expert was trained to develop a “decoder” for the simple bar task that would make it possible for the interface to translate brain waves into commands. Importantly, the decoder serves as a base for other users and is the key to avoid the long calibration process. Researchers found that the decoder worked well enough that subjects trained simultaneously for the bar game and the car racing game.
This study is a first step in brain-computer interface innovation. In subsequent research, the team will test their interface on people with motor impairments to apply their findings to larger groups in clinical settings. Related projects include a wheelchair that users can drive with the brain-computer interface and technology that can control rehabilitation robots for the hand and arm.
“On the one hand, we want to translate the BCI to the clinical realm to help people with disabilities,” said José del R. Millán, professor at the University of Texas at Austin. “On the other, we need to improve our technology to make it easier to use so that the impact for these people with disabilities is stronger.”
The new solution explored in this study would allow for quick understanding of an individual subject and self-calibration through repetition, meaning multiple patients could use the device without needing to tune it to the individual.
“The point of this technology is to help people, help them in their everyday lives,” said Millán. “We’ll continue down this path wherever it takes us in the pursuit of helping people.”