Researchers from Johns Hopkins University have demonstrated a brain computer interface that helps a paralyzed man operate devices at home. The patient could control everyday electronics, such as lights and a YouTube app, through neural signals. The study appeared in Advanced Science and spotlights a new approach to translating brain activity into practical computer commands.
Back in 2014, Tim Evans was diagnosed with amyotrophic lateral sclerosis, a progressive nervous system disease that gradually weakens muscles and can impact speaking and swallowing. In a recent study, scientists described surgically implanting a CortiCom device in brain regions tied to speech and arm movement. The device uses two small, grid-like sheets embedded with thousands of tiny sensors that pick up electrical activity from brain cells. This setup aims to capture patterns related to how the brain plans and executes movement and speech.
To turn those electrical signals into usable commands, a dedicated algorithm was designed. The patient, now 62 years old, gained reliable access to six basic actions: up, down, left, right, home, and back. These controls let him navigate interfaces and operate home systems, including lighting and media apps. Across a three-month trial, the command interpretation reached about 90 percent accuracy, and crucially, it did not require ongoing retraining or recalibration.
The researchers noted that not needing to retrain the algorithm means the neural interface can be used more freely by patients. It reduces the need for constant clinician involvement while still providing dependable control over devices in different environments.
Looking ahead, the team envisions a future where individuals with severe paralysis begin their day by turning on lights and accessing news or entertainment using only internal brain signals. This vision points toward more autonomous daily living powered by brain computer interfaces.
In related progress, scientists have explored molecular scaffolding strategies to support recovery from spinal cord injuries, underscoring ongoing advances across neural interfaces and regenerative approaches.