A tiny brain chip converts thought-to-text with 91% accuracy.
The so-called brain-machine interface (BMI), designed to be inserted into a patient’s head, appears much smaller than Elon Musk’s Neuralink.
It works by decoding neural signals generated when a person imagines writing letters or words.
The Miniaturised Brain-Machine Interface (MiBMI) chipset then processes these signals in real time, translating the brain’s intended hand movements into corresponding digital text.
BMIs have emerged as a promising solution for restoring communication and control to individuals with severe motor impairments.
The chip has been developed by researchers at Switzerland’s Ecole Polytechnique Federale de Lausanne (EPFL).
Traditionally, these systems have been bulky, power-intensive, and limited in their practical applications. Researchers at EPFL have developed the first high-performance MiBMI, offering an extremely small, low-power, highly accurate, and versatile solution.
Whereas Neuralink is about the size of a one-pound coin, the new system uses small chips with a total area of 8mm2.
Published in the latest issue of the IEEE Journal of Solid-State Circuits and presented at the International Solid-State Circuits Conference, the MiBMI not only enhances the efficiency and scalability of brain-machine interfaces but also paves the way for practical, fully implantable devices.
The technology holds the potential to significantly improve the quality of life for patients with conditions such as amyotrophic lateral sclerosis (ALS) and spinal cord injuries.
The MiBMI’s small size and low power are key features, that make the system suitable for implantable applications. Its minimal invasiveness ensures safety and practicality for use in clinical and real-life settings. It is also a fully integrated system, meaning that the recording and processing are done on two extremely small chips with a total area of 8mm2.
This is the latest in a new class of low-power BMI devices developed at Mahsa Shoaran’s Integrated Neurotechnologies Laboratory (INL) at EPFL’s IEM and Neuro X institutes.
“MiBMI allows us to convert intricate neural activity into readable text with high accuracy and low power consumption. This advancement brings us closer to practical, implantable solutions that can significantly enhance communication abilities for individuals with severe motor impairments,” says Shoaran.
Produced in association with SWNS Talker
