Next-generation neural interface: ALS patient transmits nearly 2 million words using the power of thought
Advances in neural interfaces are reaching a fundamentally new level. Researchers from the University of California, Davis have presented the results of long-term home use of a speech BCI device by a patient with amyotrophic lateral sclerosis (ALS). Over 19 months, the patient, named Casey Harrell, transmitted 183,060 sentences — that's 1,960,163 words — at an average speed of 56 words per minute. The key point: the system operated not in sterile laboratory conditions, but in everyday life, without the constant presence of researchers.
What home use showed
Harrell used the neural interface at home for over 3,800 hours. The system became his primary tool for communicating with family, friends, colleagues, and doctors. He sent messages and emails, participated in video calls, used the internet, and maintained full-time employment despite paralysis. According to the patient's own assessment, the system decoded 92% of sentences as at least "mostly correct." In formal tests where Harrell was shown words on a screen, accuracy exceeded 99% with a vocabulary of 125,000 English words. The peak rate was 99.2%. After assistants were allowed to independently connect and disconnect the equipment without researchers present, the average interaction time increased from 3.7 to 9.5 hours per day.
How the system works
In 2023, Harrell had four microelectrode arrays implanted in the left precentral gyrus — the brain region associated with speech coordination. The system reads signals from 256 cortical electrodes when the patient attempts to speak. An algorithm converts neural activity into phoneme probabilities every 80 ms, and then a language model selects the most likely sequence of words from a vocabulary of approximately 125,000 words. The text is displayed on a screen in real time. After completing a phrase, the system can vocalize it with a synthesized voice, tuned to sound like Harrell's voice before his illness. The patient also used a cursor decoder to control the computer. Previously, it was thought that speech and movement might require different brain areas, but the team showed that both modes can be implemented through signals from the speech motor cortex.
From demonstration to everyday tool
The team had already demonstrated high accuracy of the neural interface in laboratory sessions. In 2024, during the first 30-minute training session, the system achieved 99.6% accuracy with a vocabulary of 50 words. On the second day, after an additional 1.4 hours of tuning, accuracy was 90.2% with a vocabulary of 125,000 words. The new work shifts the focus from controlled trials to long-term use in a home setting. This is one of the key steps toward practical neural interfaces for people with severe motor impairments. Study co-author Sergey Stavisky noted that 3,800 hours of brain activity recording during system use became the largest individual dataset with resolution at the level of individual neurons.
The authors emphasized that the study describes a single clinical case. It is not yet known how transferable the results are to other patients, implantation sites, electrode types, and neurological conditions. The system also remains experimental. It uses wired connections, requires daily setup by trained assistants, and due to the size of the equipment, is only suitable for home use.
My comment: This breakthrough demonstrates that neural interfaces are transitioning from laboratory curiosities to real tools for improving quality of life. However, as with any experimental technology, the path to widespread adoption will be long and will require solving issues related to portability, autonomy, and calibration to individual brain characteristics.