Nature Machine Intelligence<\/strong><\/a>, 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.<\/p>\nThe team developed custom algorithms to decode electroencephalography, or EEG \u2014 a method of recording the brain\u2019s electrical activity \u2014 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.<\/p>\n
\u201cBy using artificial intelligence to complement brain-computer interface systems, we\u2019re aiming for much less risky and invasive avenues,\u201d said study leader Jonathan Kao, an associate professor of electrical and computer engineering at the UCLA Samueli School of Engineering. \u201cUltimately, 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.\u201d<\/p>\n
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.<\/p>\n
To address these limitations, the researchers tested their new noninvasive AI-assisted BCI with four participants \u2014 three without motor impairments and a fourth who was paralyzed from the waist down. Participants wore a head cap to record EEG, and the researchers used custom decoder algorithms to translate these brain signals into movements of a computer cursor and robotic arm. Simultaneously, an AI system with a built-in camera observed the decoded movements and helped participants complete two tasks.<\/p>\n