Sep 1, 2025 06:44 PM
https://www.eurekalert.org/news-releases/1096148
INTRO: UCLA engineers have developed a wearable, noninvasive brain-computer interface system that utilizes artificial intelligence as a co-pilot to help infer user intent and complete tasks by moving a robotic arm or a computer cursor.
Published in Nature Machine Intelligence, the study shows that the interface demonstrates a new level of performance in noninvasive brain-computer interface, or BCI, systems. This could lead to a range of technologies to help people with limited physical capabilities, such as those with paralysis or neurological conditions, handle and move objects more easily and precisely.
The team developed custom algorithms to decode electroencephalography, or EEG — a method of recording the brain’s electrical activity — and extract signals that reflect movement intentions. They paired the decoded signals with a camera-based artificial intelligence platform that interprets user direction and intent in real time. The system allows individuals to complete tasks significantly faster than without AI assistance.
“By using artificial intelligence to complement brain-computer interface systems, we’re aiming for much less risky and invasive avenues,” said study leader Jonathan Kao, an associate professor of electrical and computer engineering at the UCLA Samueli School of Engineering. “Ultimately, we want to develop AI-BCI systems that offer shared autonomy, allowing people with movement disorders, such as paralysis or ALS, to regain some independence for everyday tasks.”
State-of-the-art, surgically implanted BCI devices can translate brain signals into commands, but the benefits they currently offer are outweighed by the risks and costs associated with neurosurgery to implant them. More than two decades after they were first demonstrated, such devices are still limited to small pilot clinical trials. Meanwhile, wearable and other external BCIs have demonstrated a lower level of performance in detecting brain signals reliably... (MORE - details, no ads)
INTRO: UCLA engineers have developed a wearable, noninvasive brain-computer interface system that utilizes artificial intelligence as a co-pilot to help infer user intent and complete tasks by moving a robotic arm or a computer cursor.
Published in Nature Machine Intelligence, the study shows that the interface demonstrates a new level of performance in noninvasive brain-computer interface, or BCI, systems. This could lead to a range of technologies to help people with limited physical capabilities, such as those with paralysis or neurological conditions, handle and move objects more easily and precisely.
The team developed custom algorithms to decode electroencephalography, or EEG — a method of recording the brain’s electrical activity — and extract signals that reflect movement intentions. They paired the decoded signals with a camera-based artificial intelligence platform that interprets user direction and intent in real time. The system allows individuals to complete tasks significantly faster than without AI assistance.
“By using artificial intelligence to complement brain-computer interface systems, we’re aiming for much less risky and invasive avenues,” said study leader Jonathan Kao, an associate professor of electrical and computer engineering at the UCLA Samueli School of Engineering. “Ultimately, we want to develop AI-BCI systems that offer shared autonomy, allowing people with movement disorders, such as paralysis or ALS, to regain some independence for everyday tasks.”
State-of-the-art, surgically implanted BCI devices can translate brain signals into commands, but the benefits they currently offer are outweighed by the risks and costs associated with neurosurgery to implant them. More than two decades after they were first demonstrated, such devices are still limited to small pilot clinical trials. Meanwhile, wearable and other external BCIs have demonstrated a lower level of performance in detecting brain signals reliably... (MORE - details, no ads)