EPFL researchers have developed a next-generation, miniaturized brain-machine interface (BMI) that is capable of direct brain-to-text communication on tiny silicon chips—no external computation required.
According to the researchers, the "MiBMI" (miniaturized BMI) chip enhances the efficiency and scalability of brain-machine interfaces and paves the way for practical, fully implantable devices. The MiBMI's small area (8 millimeters square) and low power requirement make the system suitable for clinical and real-life applications.
Converting thoughts into readable text on a screen simply by thinking about writing
Brain-to-text conversion involves decoding neural signals generated when a person imagines writing letters or words. With current BMI systems, an external computer is required to process this data.
Instead, the researchers discovered that when a patient imagines "writing" characters by hand, it generates specific markers, or "distinctive neural codes" (DNCs), each in about 100 bytes. Using electrodes implanted in the brain, the MiBMI chipset processes these signals in real-time, translating the brain’s intended hand movements into corresponding digital text—no need to process thousands of bytes of data for each letter.
This makes the system fast and accurate, with low power consumption. It also allows for faster training times, making learning how to use the BMI easier and more accessible—especially for those with "locked-in" (unable to communicate) syndrome and other severe motor impairments.
Research collaborations
“While the chip has not yet been integrated into a working BMI, it has processed data from previous live recordings, such as those from the Shenoy lab at Stanford, converting [mental] handwriting activity into text with an impressive 91% accuracy,” said lead author Mohammed Ali Shaeri in a statement. The chip can currently decode up to 31 different characters, an achievement unmatched by any other integrated system, he notes.
The researchers say this neurotechnological breakthrough is a feat of extreme miniaturization that combines expertise in integrated circuits, neural engineering, and artificial intelligence. They are collaborating with other research groups to test the system in different contexts, such as speech decoding and movement control. "Our goal is to develop a versatile BMI that can be tailored to various neurological disorders, providing a broader range of solutions for patients,” says Shoaran.
This research is published in the current issue of IEEE Journal of Solid-State Circuits.
Citation: M. A. Shaeri, U. Shin, A. Yadav, R. Caramellino, G. Rainer, M. Shoaran, “A 2.46mm2 Miniaturized Brain-Machine Interface (MiBMI) Enabling 31-Class Brain-to-Text Decoding”, in IEEE Journal of Solid-State Circuits (JSSC), 2024, doi: 10.1109/JSSC.2024.3443254