Brain-computer interface with more microneedles

Artistic representation of the flexible, conformable, and transparent support of the new brain-computer interface with developed penetrating micro-needles. The small illustration on the lower left shows the technology currently in use on an experimental basis called Utah Arrays.

PHOTO COURTESY OF TSHADI DAYEH/UC SAN DIEGO/SAYOSTUDIO

Breakthrough brain-computer interface (BCI) With a flexible, moldable backing and penetrating microneedles, allows the device to conform to the complex curved surface of the brain and evenly distribute the microneedles that pierce the cortex. The micro-needles – 10 times thinner than human hair – protrude from the flexible backing, penetrate the surface of brain tissue without piercing surface venules, and evenly record signals from nearby nerve cells over a wide area of ​​the cortex.

This new BCI – developed by a team led by engineers from the University of California San Diego in electrical engineering professor Shadi Dayeh’s lab with Boston University researchers led by biomedical engineering professor Anna Devor – is on par with and surpasses the Utah Array, the existing gold standard for BCIs with penetrating micro-needles. The difference: The Utah Array has a tough, inflexible backing.

The flexibility and conformability of the new micro-needle array support promotes closer contact between the brain and the electrodes, allowing better and more consistent recording of brain activity signals. Working with rodents as model species, the researchers demonstrated stable broadband recordings producing robust signals throughout the duration of implantation – 196 days.

The way soft-backed BCIs are made allows for larger sensing surfaces, which means a much larger area of ​​the brain surface can be monitored simultaneously. A penetrating array with 1,024 microneedles was shown to successfully record signals triggered by precise stimuli from the brains of rats – 10 times more microneedles and 10 times the brain coverage area, compared to to current technologies. These BCIs are thinner and lighter than traditional glass media and are also transparent. The researchers demonstrate that this transparency can be leveraged to perform basic neuroscience research involving animal models that would otherwise not be possible.

All of this was achieved using double-sided lithography.

Double-sided lithographic manufacturing

Starting with a rigid silicon wafer, the team’s fabrication process builds microscopic circuits and devices on both sides of the rigid silicon wafer. On one side, a flexible and transparent film is added above the silicon wafer. In this film, a bilayer of titanium and gold traces are embedded so that the traces line up with where the needles will be made on the other side of the silicon wafer.

Working from the other side, after the addition of the flexible film, all of the silicon is etched except for the thin, pointed free-standing silicon columns – the micro-needles – and their bases line up with the traces of titanium-gold in the flexible layer that remains. These titanium-gold traces are patterned via standard and scalable microfabrication techniques, allowing for scalable production with minimal manual labor. The manufacturing process offers the possibility of flexible array design and scalability to tens of thousands of microneedles.

Towards closed-loop systems

Looking to the future, penetrating microneedle arrays with wide spatial coverage will be needed to improve brain-machine interfaces to the point that they can be used in closed-loop systems to help highly mobile people. limited. For example, providing a person using a robotic hand with real-time tactical feedback on objects gripped by the robotic hand. The robotic hand’s touch sensors would detect an object’s hardness, texture, and weight, register it, and then translate electrical stimulation patterns through wires outside the body to the brain interface. -computer. The electrical signals would deliver information directly to the person’s brain, and the person would adjust their grip strength based on sensed information directly from the robotic arm.

University of California San Diego
https://ucsd.edu