Brain-Computer Interface and AI Let Speechless ALS Patient Speak and Work Full-Time

AI and brain-computer interface allow speechless ALS patient to work a full-time job

Imagine being paralyzed so badly that not only can't you move your hands or feet, but you can't speak either. For years, brain computer interfaces have presented the tantalizing promise of reading brainwaves well enough to allow a person to communicate and access a PC. Now, a new breakthrough shows how someone can talk and even work a job while afflicted with a motion-robbing disease. A team of scientists from the University of California, Davis, published a paper Monday detailing a years-long study of a brain computer interface (BCI) system implanted in a patient with amyotrophic lateral sclerosis (ALS, also known as Lou Gehrig's disease), which destroys motor neurons and causes loss of motor control and eventual paralysis. According to the team, their patient, Casey Harrell, has been living with BCI implants since 2023 that are still working today, giving him the ability not only to control a computer cursor with his thoughts, but also to speak. The Davis team is part of a broader coalition of universities with the US Department of Veterans Affairs known as BrainGate. They're working on a variety of neuroscience projects to do things like restore speech, use computers, and, in some cases, restore movement. In Harrell's case, the Davis team was trying to figure out how to turn experimental tech into something long lasting and practical for use outside of a laboratory. Davis neurosurgeon David Brandman, co-principal investigator and co-senior author of the paper published Monday, as well as the surgeon who placed Harrell's implant, described the results his team published as the crossing of a threshold in BCI technology: Not only has Harrell's implant been working well with daily use since 2023, but it's also incredibly accurate. In controlled tests, the system managed to synthesize sentences from Harrell's brain activity with 99 percent accuracy; outside of the lab in daily use, Harrell still assessed it as being accurate 92 percent of the time. "The key thing to me is that it's enabling everyday communication for a guy who wants to talk but can't," Brandman told The Register in an interview. "Despite being paralyzed [Harrell] has gone back to work full time and has meaningful conversations with his daughter who's never heard the sound of his voice." Prior work in the BCI space, Brandman told us, has either required researchers to be in a patient's home whenever they're using the tech, or for the patient to come to the researchers. That's not the case here, with the system allowing Harrell's home care team to hook him up to the system themselves, enabling him to use the device for more than 3,800 hours in the past few years. Based on the time the study was filed (It published Monday but went into peer review in July 2025) that would mean Harrell was using the device for more than five hours a day, on average. "It is a life that is more full of dynamic action and with friends and family, with colleagues, and it is something that allows me to communicate more in my natural way of communicating than any other technology that I have experienced," Harrell told UC Davis via his BCI system. An actual practical use of AI Brandman is no stranger to BCI technology: Along with being a key figure in the BrainGate consortium, he's also worked as study principal in investigating the safety of commercial BCI tech from Paradromics, one of the leading companies in the space alongside Synchron and Neuralink. As Brandman explained it, the Davis study didn't involve any purpose-built hardware, instead making use of an existing BCI design produced by Blackrock Neurotech. The big advancement, says the Davis neurosurgeon, is with his team's use of machine learning technology. The lab has built its own software platform for operating BCI devices known as Brain-computer interface for Rapidly Adaptive Neural Decoding (BRAND, which Brandman told us was coincidentally named), which UCD postdoctoral fellow Nick Card built machine learning algorithms for. BRAND is now used across the BrainGate consortium, and is where the secret sauce of the project's success lies. According to the paper, BRAND's AI algorithms are able to translate activity in Harrell's ventral precentral gyrus, the part of the brain that controls motor function in the face, mouth, and jaw, into English-language phonemes. Additional algorithms in the software map those phonemes to words, and words to sentences. The end result is some very precise speech synthesis that allows Harrell to work full time as an environmental advocate. As for when the technology being developed by the UCD team might hit the commercial market, Brandman tells us that other technologies in the BCI space, such as those from Neuralink and others, are all working on tech with the same sorts of goals. His team's objective is just to prove that BCI systems are more than just dead-end laboratory experiments. "My job is to derisk it," Brandman told us. He likened the current state of BCI technology to early pacemakers, which started off in the 1950s having to be wired to hardware outside the body that was often connected to large batteries or directly tethered to the wall. Fast forward seventy years, and pacemakers are so simple to implant they're often done in an outpatient procedure. "We're at the early stages of this kind of technology," Brandman said. "Casey has demonstrated that this kind of tech is practical." Harrell may be wired up to a bunch of bulky external computers now, but combine the Davis UCD team's AI advancements with the hardware work being done by other firms, and the future looks brighter for a lot of people whose lives are limited by paralysis and other impairments. "I want desperately to not be unique or special, because that will mean I no longer have the disease or that everyone that has the disease like me can get [BCI] prescribed to them," Harrell said. BrainGate is currently accepting applications for future study participants. ®

UC Davis brain implant lets ALS patient speak with 99% accuracy and work full time, no researchers needed

A UC Davis BCI implant let an ALS patient speak independently for 3,800+ hours over two years with 99% accuracy, enabling him to work full time. A man with ALS has been using a brain implant to speak independently for more than 3,800 hours over the past two years, producing nearly 2 million words with an average speed of 56 words per minute. The study, published Monday in Nature Medicine by researchers at the University of California, Davis, represents the longest sustained demonstration that a brain-computer interface can function as a practical daily communication tool outside a laboratory. Casey Harrell, the 47-year-old participant, has used the system to return to full-time work as an environmental advocate. The implant consists of four microelectrode arrays placed in Harrell's left precentral gyrus, the brain region that coordinates speech, recording activity from 256 cortical electrodes. Machine learning algorithms built into a software platform called BRAND, developed by UC Davis postdoctoral fellow Nicholas Card, translate that neural activity into English-language phonemes, then map those phonemes to words and sentences. The system reads out the decoded text in a synthesised version of Harrell's pre-ALS voice. In controlled testing with a 125,000-word vocabulary, the system scored over 99% word accuracy. In daily use outside the lab, Harrell rated 92% of sentences as accurate or mostly correct. During the study period, he communicated more than 183,000 sentences. "The key thing to me is that it's enabling everyday communication for a guy who wants to talk but can't," neurosurgeon David Brandman, who implanted the device in 2023 and co-led the study, told The Register. "Despite being paralysed, he has gone back to work full time and has meaningful conversations with his daughter who's never heard the sound of his voice." The study's significance lies not just in accuracy but in independence. Previous BCI systems required researchers to be present in the patient's home whenever the device was in use, or required the patient to travel to a lab. Harrell's system is operated by his home care team, with no researcher support needed. Based on the study's timeline, he averaged more than five hours of daily use. The UC Davis team is part of BrainGate, the consortium of universities and the US Department of Veterans Affairs developing brain-computer interfaces for speech restoration, computer control, and movement recovery. The hardware itself is not custom-built, using existing microelectrode arrays produced by Blackrock Neurotech. The breakthrough is in the software, specifically the BRAND platform's machine learning algorithms that decode attempted speech from neural signals in real time. Brandman compared the current state of BCI technology to early pacemakers, which in the 1950s required external wiring to large batteries or wall power. Seventy years later, pacemakers are implanted in outpatient procedures. "We're at the early stages of this kind of technology," Brandman said. Harrell is still wired to external computers, but the UC Davis team's AI advances combined with hardware miniaturisation work at companies like Neuralink, Synchron, and Paradromics point toward a future where the setup is far less cumbersome. The competitive landscape in BCI is accelerating. Neuralink has implanted devices in at least 21 patients under research protocols but lacks commercial approval. China approved the first commercially available invasive BCI earlier this year. Other approaches to restoring speech for people with ALS use AI voice conversion rather than brain implants, but those methods require the patient to retain some vocal ability. What distinguishes the UC Davis work is its demonstration that a BCI can cross from laboratory experiment to sustained, practical daily tool. The 3,800 hours of brain recording also constitute the largest individual neural dataset with single-neuron resolution ever collected, according to co-principal investigator Sergey Stavisky, which will inform future improvements to the decoding algorithms. The system remains an investigational device, limited by federal law to research use, and has been tested on a single patient. Whether the results generalise to other ALS patients, or to people with different neurological conditions, is not yet known. Scaling the technology from a clinical trial to a prescribed medical device will require regulatory approval, hardware miniaturisation, and cost reduction that could take years. "I want desperately to not be unique or special, because that will mean I no longer have the disease or that everyone that has the disease like me can get this prescribed to them," Harrell said through his BCI system.