The Layer 7 Cortical Interface: A Scalable and Minimally Invasive Brain-Computer Interface Platform

Progress toward the development of brain-computer interfaces has signaled the potential to restore, replace, and augment lost or impaired neurological function in a variety of disease states. Existing approaches to developing high-bandwidth brain-computer interfaces rely on invasive surgical procedures or brain-penetrating electrodes, which limit addressable applications of the technology and the number of eligible patients. Here we describe a novel approach to constructing a neural interface, comprising conformable thin-film electrode arrays and a minimally invasive surgical delivery system that together facilitate bidirectional communication with large portions of the cortical surface (enabling both recording and stimulation). We demonstrate the feasibility and safety of delivering reversible implants containing over 2,000 microelectrodes to multiple functional regions in both hemispheres of the brain simultaneously, without requiring a craniotomy or damaging the cortical surface, at an effective insertion rate faster than 40 ms per channel. We further evaluate the performance of this system immediately following implantation for high-density neural recording and visualizing cortical surface activity at spatial and temporal resolutions and extents not previously possible in multiple preclinical large animal studies as well as in a five-patient pilot clinical study involving both anesthetized and awake neurosurgical patients. We characterize the spatial scales at which sensorimotor activity and speech are represented at the cortical surface, demonstrate accurate neural decoding of somatosensory, visual, and volitional walking activity, and achieve precise neuromodulation through cortical stimulation at sub-millimeter scales. The resulting system generates 90 Gb/h of electrophysiologic data, and demonstrates the highly scalable nature of micro-electrocorticography and its utility for next-generation brain-computer interfaces that may expand the patient population that could benefit from neural interface technology. ### Competing Interest Statement This work was funded by Precision Neuroscience Corporation.