Traditionally, electrical brain-computer interfaces (BCIs) have required the assembly of two separate components: electrodes for interfacing with tissue and electronics for signal acquisition and stimulation. Furthermore, these electronics required cabled connections to workstations for data processing and control. Efforts to overcome these limitations have made significant progress in the last decade. Now, there are in vivo validated monolithic electrophysiological BCI devices, exemplified by the Neuropixels, that integrate the two components onto a single platform. At the same time, a new generation of fully wireless BCI devices that reside entirely under the skin has been developed.
Despite these advancements, the current state-of-the-art BCIs have yet to overcome both challenges simultaneously. Multi-channel, high-bandwidth monolithic BCIs still require percutaneous wired connections, whereas wireless BCIs rely on the assembly of discrete components that result in bulky form factors. The next generation of BCIs calls for a new paradigm that integrates electrodes and electronics into a miniaturized form factor while supporting a fully wireless operation.
This thesis contributes to the collaborative effort that presents such a paradigm through the development of a wireless, battery-free micro-electrocorticography (μECoG) device that monolithically integrates electrodes, signal processing, data telemetry, and powering onto a single complementary metal-oxide-semiconductor (CMOS) substrate. The device contains 65,536 recording and 16,384 stimulation channels, from which a programmable subset of up to 1024 channels can berecorded at a given time. Implemented in a mechanically flexible, 50-μm-thick form factor with a total volume of only 7.2 mm³, the device is implanted entirely in the subdural space and conforms to the contour of the cortical tissue surface. A custom "relay station" provides wireless powering and bi-directional communication to the implant from outside the body.
The system was validated through a series of proof-of-concept in vivo recordings from different cortical regions of a pig and non-human primates, reliably decoding brain signals at high spatiotemporal resolution. By using a unique, fully integrated architecture, the BCI developed in this work achieves orders-of-magnitude improvements in volumetric efficiency and channel count over existing approaches, setting a milestone for the next generation of BCI devices.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/pd32-w056 |
| Date | January 2024 |
| Creators | Jung, Taesung |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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