Brain-computer interface with more microneedles

Two-sided lithographic manufacturing improves high-resolution brain recording.

Artist rendition of the flexible, conformable, transparent backing of the new brain-computer interface with penetrating microneedles developed. The smaller illustration at bottom left shows the current technology in experimental use called Utah Arrays.
PHOTO COURTESY OF TSHADI DAYEH/UC SAN DIEGO/SAYOSTUDIO

An advanced brain-computer interface (BCI) with a flexible and moldable backing and penetrating microneedles, allows the device to conform to the brain’s complex curved surface and uniformly distribute the microneedles that pierce the cortex. The microneedles – 10x thinner than human hair – protrude from the flexible backing, penetrate the surface of the brain tissue without piercing surface venules, and record signals from nearby nerve cells evenly across a wide area of the cortex.

This new BCI – developed by a team led by engineers at the University of California San Diego in the laboratory of electrical engineering professor Shadi Dayeh with researchers at Boston University led by biomedical engineering professor Anna Devor – is on par with and outperforms the Utah Array, the existing gold standard for BCIs with penetrating microneedles. The difference: the Utah Array has a hard and inflexible backing.

The flexibility and conformability of the backing of the novel microneedle-array favors closer contact between the brain and the electrodes, allowing for better and more uniform recording of the brain-activity signals. Working with rodents as model species, the researchers have demonstrated stable broadband recordings producing robust signals for the duration of the implant – 196 days.

The way the soft-backed BCIs are manufactured allows for larger sensing surfaces, which means that a significantly larger area of the brain surface can be monitored simultaneously. It was demonstrated that a penetrating array with 1,024 microneedles successfully recorded signals triggered by precise stimuli from the brains of rats – 10x more microneedles and 10x the area of brain coverage, compared to current technologies. These BCIs are thinner and lighter than the traditional glass backings and are also transparent. The researchers demonstrate this transparency can be leveraged to perform fundamental neuroscience research involving animal models that otherwise wouldn’t be possible.

This was all achieved using double-sided lithography.

Two-sided lithographic manufacturing

Starting from a rigid silicon wafer, the team’s manufacturing process allows building microscopic circuits and devices on both sides of the rigid silicon wafer. On one side, a flexible, transparent film is added on top of the silicon wafer. Within this film, a bilayer of titanium and gold traces is embedded so the traces line up with where the needles will be manufactured on the other side of the silicon wafer.

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

Toward closed-loop systems

Looking to the future, penetrating microneedle arrays with large spatial coverage will be needed to improve brain-machine interfaces to the point they can be used in closed-loop systems to help individuals with severely limited mobility. For example, offering a person using a robotic hand real-time tactical feedback on the objects the robotic hand is grasping. Tactile sensors on the robotic hand would sense the hardness, texture, and weight of an object, record it, and then translate into electrical stimulation patterns traveling through wires outside the body to the brain-computer interface. The electrical signals would provide information directly to the person’s brain and the person would adjust their grasp strength based on sensed information directly from the robotic arm.

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

June 2022
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