Automated Hooking of Biomedical Microelectrode Guided by Intelligent Microscopic Vision

Abstract

Biomedical microelectrode implantation is a promising approach to achieve invasive brain-machine interface with high bandwidth and good biocompatibility. However, it is difficult to manipulate a microelectrode probe because of its micron-level width and flexible material. Toward the challenging robotic implantation, this article addresses an essential operation step: automated hooking of flexible microelectrode probe with a micro-needle. First, a point-contour of interest extraction network for microscopic image (MicroPCIE) is proposed to obtain key image features for visual guidance. Based on few-shot learning mechanism, MicroPCIE trained on nonspecific dataset can be rapidly deployed to the specific hooking task without requiring data-driven optimization. Second, a robotic hooking control system is designed considering the distributed probe locations, variable needle direction, and efficiency requirements. The needle-probe alignment control is executed under low and high microscope magnifications, to realize larger operation range and permit high alignment precision. The optical design and camera aiming control are proposed to guarantee the visibility and clearness of objects after low-to-high magnification switch. The effectiveness of the proposed methods is validated with a series of experiments.