Visual Studio 2010与Matlab混合编程的研究及其在BCI系统中的应用

发布时间：2018-03-03 20:16

本文选题：Matlab　切入点：Visual　出处：《山东师范大学》2017年硕士论文　论文类型：学位论文

【摘要】：对脑电信号进行研究是脑科学研究领域的重要内容,在这一领域,脑机接口(Brain Computer Interface,BCI)系统既涉及医学学科又涉及计算机通信与控制等学科,已成为这一领域中的热点课题。采集到的脑电信号在经过硬件处理之后可能仍然包含有大量的噪声,为了保证信号后续处理分析结果的精准性,需要对信号进行滤波等预处理。Kalman滤波是一种高效率滤波方法,能适应多种不同条件的滤波工作,被广泛地使用在信号处理工作、导航、自动控制等领域。本文使用Kalman滤波对采集到的脑电信号进行了滤波处理,经后续试验表明利用其对脑电信号进行预处理可以提高对信号特征的识别率。本文通过分析Matlab与C\C++等高级编程语言的优缺点,使用了Matlab与C++两者优势互补的混合编程的方式实现了Kalman滤波,该方法既实现了Matlab强大的数值方面运算能力的跨平台应用,又有高级编程语言执行效率高,界面编写功能强大的优点,并将Kalman滤波器应用于脑电信号的预处理,取得了较好的效果。本篇论文的主要工作有以下几点:1.本文介绍了脑电信号采集与处理中的脑机接口系统的大致组成及其工作流程,详细介绍了本文在脑机接口系统信号处理模块用到的Kalman滤波算法及ST(S-transform,S变换)算法和梯度Boosting(Gradient Boosting,GB)算法相结合的特征提取和分类识别算法的基本原理。2.本文阐述了Matlab与VC++的优点及缺点,介绍了相关文献中前人整理过的传统的VC++6.0这一工具与Matlab混合编程的几种方式,并介绍了当前最新的VC++标准:C++0x/11标准以及VC++6.0的问题及与VS的区别。在此基础上,实现了新版的Visual studio 2010与Matlab R2012b的混合编程。3.通过实例将Visual studio 2010与Matlab R2012b混合编程的思想应用到了脑机接口系统中,在对脑机接口系统预处理模块中的信号滤波后,在此基础上使用了ST算法对采集到的运动想象信号进行了特征提取,并使用GB算法进行了识别分类,并通过实验验证了方法的有效性。
[Abstract]:The research of EEG is an important part in the field of brain science. In this field, the Brain Computer Interface (BCI) system involves not only medical disciplines, but also computer communication and control. It has become a hot topic in this field. The collected EEG signals may still contain a lot of noise after the hardware processing. In order to ensure the accuracy of the analysis results of the subsequent processing of the signals, Kalman filtering is a high efficient filtering method, which can adapt to many different conditions of filtering, and is widely used in signal processing, navigation, navigation, and so on. In this paper, Kalman filter is used to filter the collected EEG signal. The subsequent experiments show that the preprocessing of EEG signals can improve the recognition rate of the signals. This paper analyzes the advantages and disadvantages of advanced programming languages such as Matlab and C\ C. The Kalman filter is realized by using the hybrid programming method of Matlab and C, which not only realizes the cross-platform application of Matlab's powerful numerical computing ability, but also has high execution efficiency in advanced programming language. Interface programming has the advantage of powerful function, and the Kalman filter is applied to the pretreatment of EEG signal. The main work of this paper is as follows: 1. This paper introduces the general composition and workflow of the BCI system in EEG acquisition and processing. This paper introduces in detail the basic principle of feature extraction and classification recognition algorithm which is used in the signal processing module of brain-computer interface system based on the combination of Kalman filter algorithm and ST-S-transform-S transform algorithm and gradient Boosting(Gradient boost algorithm. The advantages and disadvantages of VC, This paper introduces several methods of mixed programming between VC 6.0 and Matlab, and introduces the latest VC: C 0x / 11 standard, the problems of VC 6.0 and the difference between VC 6.0 and vs. On the basis of this, the paper introduces several methods of mixed programming of VC 6.0 and Matlab, and introduces the latest VC: C 0x / 11 standard and the problem of VC 6.0 and the difference between VC 6.0 and vs. The new hybrid programming of Visual studio 2010 and Matlab R2012b is realized. 3. The idea of Visual studio 2010 and Matlab R2012b mixed programming is applied to the BCI system through an example. After the signal filtering in the BCI system preprocessing module, On this basis, St algorithm is used to extract the feature of the collected motion imagination signal, and the GB algorithm is used to identify and classify the signals. The validity of the method is verified by experiments.
【学位授予单位】：山东师范大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R318;TN911.7

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