毕业生知识库

脑机接口（Brain-Machine Interface，BMI）旨在建立大脑与计算机之间的通信渠道，实现对人脑活动的实时监测和解读。近年来，柔性电极作为侵入式脑机接口的重要组成部分，因其更好的生物相容性和稳定性受到广泛关注。然而，柔性电极植入手术存在操作难度大的问题，需要借助植入机器人来提高植入精度和效率。本文以柔性电极微创植入为具体应用，重点研究柔性电极植入过程中的识别定位、测量对准、插入控制等关键技术，旨在突破柔性电极植入瓶颈，实现柔性电极沿颅脑微孔自动植入，为脑机接口技术的应用提供技术支持。论文主要内容如下：

1. 针对显微相机图像易受离焦、遮挡和光线变化等影响的情况，提出了基于深度卷积网络的目标检测和关键点定位网络，并引入金字塔结构编码器进行特征图提取。此外，基于目标检测结果和金字塔结构特征提取器，实现了植入针针尖和柔性电极环中心点的定位，提高了关键点定位的精度和鲁棒性。
2. 提出适用于植入针和柔性电极的基于模板匹配的图像特征提取方法，以及适用于颅骨微孔的基于特征点匹配的图像特征提取方法。在图像特征提取的基础上，基于极线约束和三角化恢复了目标的三维姿态向量，并实现了对植入针、柔性电极和颅骨微孔姿态测量的误差分析。此外，设计了植入针、柔性电极和颅骨微孔的位姿对准控制流程，并建立基于图像雅可比（Jacobian）矩阵的视觉伺服控制，实现了植入针和柔性电极、植入针和颅骨微孔的自动位姿对准。
3. 针对植入针在插入颅骨微孔时可能与孔壁发生碰撞的问题，提出了一种多模态的接触状态识别网络，旨在准确识别植入针和颅骨微孔之间的相对接触状态。此外，设计了植入针插入并穿过颅骨微孔的控制流程，根据接触状态识别网络的结果预测植入针下一时刻动作，并建立位置伺服控制来调整植入针姿态，实现植入针沿颅骨微孔顺利植入的应用目标。

Brain-machine interface (BMI) aims to establish a communication channel between the brain and the computer, enabling real-time monitoring and interpretation of human brain activities. In recent years, flexible electrode, as an important component of invasive BMI, has attracted widespread attention due to their superior biocompatibility and stability. However, flexible electrode implantation surgery suffers from the problem of difficult operation, requiring the assistance of implantation robot to improve accuracy and efficiency. This paper takes minimally invasive implantation of flexible electrode as a specific application, and focuses on key technologies such as detection and localization, measurement and alignment, and insertion control during the flexible electrode implantation. The aim is to break through the bottleneck of flexible electrode implantation, realize automatic implantation of flexible electrode along skull hole, and provide technical support for the application of brain-machine interface technology. The main contents of the paper are as follows:

(1) Due to the susceptibility of microscopic camera images to factors such as defocusing, occlusion, and lighting, an object detection and keypoint localization network is proposed. In addition, a pyramid structure encoder is introduced for feature map extraction. Furthermore, leveraging the object detection results and pyramid structure feature extractor, the localization of the implanted needle tip and the center point of the flexible electrode ring is realized, which improves the accuracy and robustness of the key point localization.

(2) An image feature extraction method based on template matching is proposed for implanted needle and flexible electrode, and an image feature extraction method based on feature point matching is proposed for skull hole. On the basis of image feature extraction, the 3D attitude vector of the target is recovered based on polar constraints and triangulation, and the error analysis of the attitude measurements of implanted needle, flexible electrode and skull hole is realized. In addition, the pose alignment control process is designed and the visual servo control based on the image Jacobian matrix is established to realize the automatic pose alignment of the implanted needle and flexible electrode, and the implanted needle and skull hole.

(3) As the implanted needle may collide with the wall of skull hole during insertion, this paper proposes a multimodal contact state recognition network aimed at accurately identifying the relative contact states between the implanted needle and the skull hole. In addition, a control flow for the insertion of the implanted needle through the skull hole is designed. Based on the results of the contact state recognition network, the next moment action of the implanted needle is predicted, and the position servo control is established to adjust the implanted needle attitude, so as to achieve the application goal of smooth implantation of the implanted needle along the skull hole.