【作者】 李明钊；

【导师】 庞彦伟；

【作者基本信息】 天津大学 ， 信息与通信工程， 2012， 硕士

【摘要】 脑机接口是建立在人脑和外部设备之间的直接通讯通路。脑机接口技术结合了生物医学、计算机信息处理技术、神经科学以及微电子等多个领域的最新成果,在近10年的时间里得到的广泛的重视研究和发展。通过对脑电信号的研究,脑机接口BCI(Brain-Computer Systerm)系统已经可以被用来解决很多实际问题。尽管如此,因为脑电信号的不稳定性和个体差异性,找到一种高效的、具有普遍意义的信号处理和识别方式是解决问题的关键。本文的主要研究对象是BCI系统中脑电信号的处理和识别方法。通常一个模式识别过程可以分为数据预处理、特征提取、特征选择和降维、以及特征分类几个阶段。在脑电信号的预处理阶段使用FIR数字滤波器和CSP空域滤波器的方法对脑电信号进行滤波处理,用主成分分析PCA和偏最小二乘PLS的方法对脑电信号的特征向量进行降维,用两种不同的Adaboost分类方法对提取到的脑电信号特征向量进行分类,同时用线性判别分写LDA和支持向量机SVM的BCI系统经典分类方法进行分类识别率的对比。通过对在模式识别每一个阶段对脑电信号处理的不同方法的研究,在实验结果数据的基础上给出一种高效的处理脑电信号的方法,即公共空间模式滤波与以最近邻法为若分类其的AdaboostNN分类器结合的处理方式。更多还原

【Abstract】 Brain-machine interface is direct communication access which is based on the human brain and the external device. Brain-machine interface technology combines the latest achievements of bio medicine, computer information processing technology, neuroscience, microelectronics and other fields, and in the past 10 years, it has developed quickly. With the study of EEG,BCI system can already be used to solve many practical problems. Because of the instability of EEG and individual differences, people need to find an efficient and stable signal processing and recognition approach in BCI systerm. This paper studies the EEG processing and recognition methods of the BCI system. Usually a pattern recognition process can be divided into data preprocessing, feature extraction, feature selection and dimensionality reduction, and feature classification. In the stage of EEG preprocessing, using FIR digital filters and CSP spatial filter for EEG filter processing. Using principal component analysis PCA and partial least squares PLS to reduce dimensions of EEG feature vectors. Classification methods used here are two different Adaboost classification methods for classifying the extracted feature vectors of EEG signals. Linear discriminant analysis LDA and support vector machine SVM which are classic classification methods of BCI system are applied for comparison of the recognition rate.EEG signals are processed in a BCI system by those different methods above, based on the experimental results,this paper gives a efficient EEG signal processing method,i.e. a combination method of CSP filtering and AdaboostNN classifier.更多还原