脑-机接口（Brain Computer Interface, BCI）技术得益于计算机、通信、传感器等技术的迅猛进步，在过去的几十年里发展迅速。脑-机接口通过建立神经系统与设备的交互通路从而起到替代、修复、增强、补充及改善作用，特别是给行动障碍的患者带来了希望与便利。脑深部电刺激（Deep Brain Stimulation，DBS）疗法是治疗帕金森病的一种重要治疗方法。在可采集深脑部局部场电位（Local Field Potential，LFP）的脑起搏器植入的背景下，借助这一全植入设备实现脑-机接口功能则不仅可以帮助患者改善生活，更可以加深对大脑的认识。首先，本文分析了实现基于深部信号脑-机接口功能当前所具备的条件，在此前提下，根据脑-机接口实现所需要进行的数据采集方式，设计了植入设备与外部采集装置不同采集设备间的数据同步方法。并且，在体外同步测试中发现，由于采样率误差带来了一定的同步误差。针对采样率误差大的问题本文提出了两种改进方案：①从硬件设计上提高同步精度；②从软件上提高了同步精度。最终两种方法的同步误差均控制在了类似同步商用设备的误差量级，为数据的进一步分析提出了可能。其次，为便于将数据进行在线处理（区别于离线分析），开发了基于MATLAB（MathWorks Inc）的在线采集平台，并设计了图形用户界面。通过该项工作将脑深部LFP数据实时开放到MATLAB平台，并且通过在线分析模块实现了对数据进行实时时频分析、实时特征提取等功能，并预留了数据接口供二次开发。该工作为数据的实时分析以及使用MATLAB强大的各类工具箱创造了可能，也为脑-机接口实现搭建了上位机分析平台。最后，本文利用采集平台与数据同步方法，在抬手运动的实验范式下，对行为运动信息及LFP数据进行了采集。使用小波变换的时频分析方法对数据的特征进行了计算以及分析。结合小组的工作，设计并优化了基于支持向量机（support vector machines, SVM）、隐马尔可夫模型（HMM）以及启发式规则修正的在线分类器。在解决了特征在线计算的问题后，首次实现了基于深部LFP信号的全植入式的脑-机接口对抬手运动识别的功能，验证了可采集DBS拓展为全植入式脑-机接口的可行性。

Thanks to the development of technologies such as computers, communications, and sensors，Brain Computer Interface (BCI) technology has developed rapidly in the past decades. The brain-computer interface plays a role of replacement, repair, enhancement, supplementation and improvement by establishing an interactive pathway between the nervous system and the device, especially bringing hope and convenience to patients with mobility impairments. Deep Brain Stimulation (DBS) therapy is an important treatment method for Parkinson's disease. In the context of the implantation of a pacemaker that can collect local field potentials (LFP) in the deep brain, the use of this fully implanted device to realize the brain-computer interface function can not only help patients improve their lives, but also Deepen the understanding of the brain.First of all, this article analyzes the current conditions for the realization of the brain-computer interface function based on deep signals. Under this premise, according to the data acquisition method required for the realization of the brain-computer interface, the implantation device and the external acquisition device are designed to collect different data. The method of data synchronization between devices. Moreover, it was found in the in vitro synchronization test that a certain synchronization error was brought about by the sampling rate error. Aiming at the problem of large sampling rate error, this article proposes two improvements: ①Change the hardware module to improve the synchronization accuracy from the hardware design; ②The calibration and data resampling improve the synchronization accuracy from the software. In the end, the synchronization errors of the two methods are controlled at the level of errors similar to the synchronization of commercial equipment, which provides the possibility for further analysis of the data.Secondly, in order to facilitate online processing of data (different from offline analysis), an online acquisition platform based on MATLAB (MathWorks Inc) was developed, and a graphical user interface was designed. Through this work, the deep brain LFP data is opened to the MATLAB platform in real time, and functions such as real-time time-frequency analysis and real-time feature extraction of the data are realized through the online analysis module, and the data interface is reserved for secondary development. This work created the possibility of real-time data analysis and the use of MATLAB's powerful toolboxes, and also built a host computer analysis platform for the realization of the brain-computer interface.Finally, this article uses the collection platform and data synchronization method to collect behavioral movement information and LFP data under the experimental paradigm of raising hands. The time-frequency analysis method of wavelet transform is used to calculate and analyze the characteristics of the data. Combined with the work of the group, an online classifier based on support vector machines (SVM), hidden Markov models (HMM), and heuristic rule modification was designed and optimized. After solving the problem of feature online calculation, the fully implantable brain-computer interface based on deep LFP signals is the first to realize the function of hand-raising movement recognition, and it is verified that the DBS can be collected and expanded to a fully implanted brain-computer interface.