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脑电特征提取与在线脑机接口研究

EEG Feature Extraction and Online BCI Research

【作者】 胡备

【导师】 王金甲;

【作者基本信息】 燕山大学 , 通信与信息系统, 2012, 硕士

【摘要】 脑机接口BCI是一种新型的人机交互方式,它不依赖于大脑的正常输出通道(外围神经和肌肉组织等),实现人脑直接与外部环境的信息交互。BCI作为一种交叉学科技术,涉及到医学工程、康复工程、电子工程及人工智能等领域。BCI系统解读大脑思维,正确认识大脑各源与思维动作之间的关系尤其重要,其中脑电信号EEG的特征提取、模式分类理论方法的研究一直是热点问题。本文针对EEG信号的特征提取方法做了离线分析研究,在已有研究的基础上作了如下工作:首先,针对传统的线性判别分析中小样本采样问题,以及其核推广核线性判别分析,提出了一种基于广义奇异值分解的广义核线性判别分析。为了最大限度的释放不同类之间的非线性模式,同时解决小样本采样问题,采用一种广义奇异值分解的方法,解除了奇异性限定。其次,同样针对小样本采样问题,本文将鉴别公共矢量方法应用在脑机接口特征提取中。在脑机接口中采集的脑电信号,其样本数是远远小于或等于样本空间的维数的,也就是通常所谓的小样本采样问题。鉴别公共矢量方法中,借鉴了公共矢量的理论,即通过消除类内离散矩阵的非零特征值所对应的特征向量方向上的共有特征,该公共矢量可作为分类的特征。最后,本文针对在线BCI做了研究,实现了基于P300电位的脑机接口小车控制。本文对P300电位做了深入研究,并在BCI2000平台下,加入了外设控制命令,成功的完成在线视觉刺激BCI外部设备控制系统。受试者几乎不需要经过训练,或小量训练,便可通过视觉刺激来控制小车的动作。

【Abstract】 Brain-Computer Interface is a new way of human-computer interface.It is notdependent on the brain’s normal output channels (peripheral nerves and muscles),butdirectly change the information between the brain and outside world.As amultidisciplinary cross technology, BCI relate to Biomedical Engineering, RehabilitationEngineering, Electronics Engineering and Artificial Intelligence.BCI system can explainthe brain’ mental task, it is vary important to get the relationship between brain’s sourceand mental task, The theory research such as feature extraction, classification andexperiment research of electroencephalogram(EEG) play important roles.EEG signal can be collected conveniently, so it can be easily popularize. The maincontributions of the paper are as follows:First of all, a new method which combines GSVD and kernel method is proposed tosolve the problems of small sample size in traditional LDA and kernel LDA. In order torelax the presumption of strictly linear pattern and relieve the nonsingular, GSVD is usedto solve the optimizing spatial filter.Secondly, for the small sample size, this paper use another method calledDiscriminative Common Vector. In BCI system, the number of sample of EEG signal islow than the sample space dimension, i.e. small sample size problem. DCV removes allthe features that are in the direction of the eigenvectors corresponding to the nonzeroeigenvalues of the scatter matrix. The DCV can be the feature to be classified.At last, the online BCI is involve. A BCI car controlled system based on P300potential is realized. We makes a deep research on P300potential. With the BCI2000platform, an online visual stimulation BCI external equipment control system is realized.The research subjects can control the car by visual stimulation after a short train time.

  • 【网络出版投稿人】 燕山大学
  • 【网络出版年期】2012年 11期
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