Researchers at the University of California, Los Angeles (UCLA) have developed a wearable non-invasive brain-computer interface (BCI) using AI as a “co-pilot,” reports Neuroscience News.
The system allows for more precise and faster control of a robotic arm or cursor.
The device translates brain signals, captured via electroencephalography, into movement commands. Special algorithms have been developed to decode these impulses.
AI cameras interpret the user’s intentions in real time. This system enables tasks to be completed significantly faster than without AI support.
“By using artificial intelligence in addition to brain-computer interface systems, we aim to find much less risky and non-invasive approaches,” commented Jonathan Kao, the study’s lead researcher and assistant professor of electrical and computer engineering at UCLA’s Samueli School of Engineering.
In tests, four participants, including one paralyzed individual, completed tasks significantly faster with AI support. Some tasks would have been impossible without it.
In the first task, participants were asked to move a computer mouse cursor across the screen between eight targets, pausing for at least half a second on each. The second test required moving blocks on a table using a robotic arm.
The paralyzed participant completed the robotic arm task in about six and a half minutes with AI support, whereas without it, the task was unachievable.
Scientists believe this breakthrough will aid in developing safer and more accessible assistive technologies for individuals with paralysis or motor impairments.
“Ultimately, we want to create AI-BCI systems with combined control that will allow people with motor impairments, such as paralysis or ALS, to regain some independence in daily activities,” Kao emphasized.
Current BCI devices with surgical implants can translate brain signals into commands, but they carry high risks and are costly.
Meanwhile, wearable and external gadgets show lower reliability in recognizing brain signals.
In April, a woman paralyzed after a stroke regained her speech after 18 years of silence thanks to an experimental BCI and artificial intelligence.