【作者】 贾希；

【导师】 贾志成； 高振斌；

【作者基本信息】 河北工业大学 ， 通信与信息系统， 2007， 硕士

【摘要】 近十几年来,脑-机接口(Brain-Computer Interface)技术的研究在国际上引起了广泛的兴趣并获得了快速的发展。它可以为神经肌肉损伤患者提供一种不依赖正常的外围神经和肌肉输出通道的新的通讯工具,也可以为正常人提供辅助控制通道或在特殊环境下的控制通道,因此具有巨大的社会价值和应用前景。信号分析与处理环节是实现BCI系统、提高系统可靠性与性能的关键,也是本文研究的主要内容。本文首先介绍了脑电信号的有关概念和特性等,并对脑电信号的处理技术的研究进展进行了综述。然后重点讨论了脑电信号的特征提取和分类的方法。主要对以下几个问题做了研究和讨论:(1)通过小波变换方法对左右手运动想象脑电信号进行降噪。在对各种小波阈值降噪方法(固定阈值形式‘Sqtwolog’及硬阈值法;使用Birge-Massart惩罚函数的阈值形式及软阈值法;使用分层阈值及改进的阈值法)的讨论比较之后,给出一种改进方案。然后对不同降噪方法处理后的脑电信号用概率神经网络进行分类。实验证明该降噪方案的降噪效果优于其它方法。(2)研究了左右手想象运动任务的60导诱发脑电信号在不同时段的幅值变化规律,对将脑部热区移动轨迹作为运动想象脑电信号特征的可行性作了探讨。同时,设想了一种新方法——质心法在不同想象运动任务的脑电信号图中的应用,证明了不同时段的脑电图存在不同的质心,进一步验证了进行不同的想象思维任务的时候,热区会随着不同时间发生移动的规律,并可作为辅助分类方法。(3)研究了脑电诱发电位提取的基本方法,并利用小波多尺度分解与重建方法对诱发电位进行了提取,实验中对诱发电位进行叠加平均处理,在一定程度上提高了信噪比,实验分类效果更好。然后,利用脑电图分析实验的结果,选取热区内的左中央、右中央、左顶、右顶四个导联的信号进行了特征提取与分类实验,利用小波变换和神经网络对所选的特征进行了测试。将各层的小波系数作为分类参数,证明了选取四导联脑电信号比二导联脑电信号的处理效果更好。说明在可接受的处理复杂度内可适当地增加导联信号的选取。更多还原

【Abstract】 Over the past decade, there has been great interest and a rapid development in the research of brain-computer interface technology. BCI can provide a new communication option that does not depend on peripheral nerves and muscles for those with neuromuscular impairments, and might also give those without disabilities a supplementary control channel or a control channel useful in special circumstances. Signal analysis and processing is a key issue for implementation of a BCI system as well as improving its reliability and performance, and which is the main contents in the paper.First, a summary of the relating concepts of EEG and its statistics nature was given in chapter 2, which are the fundamentals for farther discussion. Then, the extraction and classification methods discussed at emphasis. The main research work of this paper focuses on the application of wavelet transform technique in EEG analysis, which could be summarized as the following three aspects. (a) In chapter 3 a novel threshold is proposed for wavelet threshold de-noising method. First,hard threshold, soft threshold and the proposed threshold are used for de-noising and decomposing of the EEG data. And then the wavelet coefficient is used as extracted feature set and is fed to a probabilistic neural network classifier to organize the EEG signals into different activities. It is shown that the novel threshold is better than the last two for EEG signal de-noising based on wavelet transform. (b) Through research the 60 channels EP amplitude variation rule in different times of left-right hand imagine movement tasks, the feasibility of using the brain hot space remove track as the feature of the imagine movement EEG is discussed. At the same time, a new method--barycentric method is given in the paper. It proves that there are different centroids in different electroencephalogram, and this method can be used in assistant classification. (c) The basic method is given in chapter 4, and the superposed mean value algorithm is used in EP preprocessing, which can improve the signal-to- noise ratio (SNR) to the extent, and improve the classified result. Then, based on the result of the encephalogram analysis experiment, the 4 channels EEG are accessed to the extraction and classification, which are C3,C4,P3,P4, and wavelet transform and probabilistic neural network are used for testing. It can be approved that processing on 4 channels EEG is better than 2 channels EEG. So increasing the channels accessing in appropriate processing order of complexity is acceptable.更多还原