【作者】 刘洋；

【导师】 邱天爽；

【作者基本信息】 大连理工大学 ， 信号与信息处理， 2006， 硕士

【摘要】 脑电图反映了大脑组织的电活动及大脑的功能状态。脑电分为自发脑电(EEG)和诱发电位(EP)两种。作为脑电信号研究的第一步,EEG中伪差的去除及EP信号的提取就显得尤为重要。 独立分量分析(ICA)是近年来由盲信源分解技术发展来的多通道信号处理方法,可以帮助实现信号的增强和分析。对于多导脑电诱发电位的提取得到了较满意的结果。另一方面,ICA已经逐渐被应用于EEG信号中的伪迹去除,实践证明其能有效的解决应用传统方法产生的一系列问题。 遗传算法是模仿自然界生物进化机制发展起来的随机全局搜索和优化方法,通过模拟自然进化过程来搜索最优解,在搜索过程中不容易陷入局部最优,即使在所定义的适应度函数是不连续的、非规则的或有噪声的情况下,也能以很大的概率找到全局最优解。本文提出了一种基于独立分量分析和遗传算法的诱发电位提取方法,以EP信号的峭度作为目标函数基准点,以其它信号的峭度与基准点的偏离程度的倒数作为目标函数,通过遗传算法在全局搜索适应度函数的最大值,从而首先将EP信号分离出来,实现了诱发电位的快速提取。由于此种方法是用遗传算法来接解决ICA问题,所以使得它具有遗传算法的一些特性,如收敛性强等特点。与其他快速提取EP信号特征的方法(如rICA)相比,本文的方法不需要引入参考信号,计算简单。 由于临床采集的脑电信号中含有较丰富的频率成分,只靠ICA方法有时无法将脑电信号中的噪声分离干净,分离后的脑电信号中往往还含有很多高频噪声。如果在用ICA方法对信号进行处理前,先利用小波变换的滤噪作用,将混合在原始脑电的高频噪声滤除后,再重构原始脑电作为ICA的输入信号,所分离出的脑电信号中高频噪声就会大大减少,可以有效克服现有ICA算法不能区分噪声的缺陷。本文将小波变换与基于遗传算法的独立分量分析方法相结合,用于诱发电位的提取,取得了较满意的效果。更多还原

【Abstract】 Brain signal reflects the electricity activities of cerebral tissue and brain function status. Brain signals are classified into EEG signal and EP signal. As the first step of research on brain signals, the removal of artifacts in EEG signals and the extraction of EP signals play an important role.ICA is a multi-channel signal processing method derived from Blind Source Separation. It helps to achieve the enhancement and analysis of signals. We have acquired satified results as to multi-channel exaction in visual evoked potential. On the other hand, ICA is gradually applied to removal of artifacts in EEG and has resolved many problems resulted from traditional method in practical use.Genetic algorithm is based on the evolutionarily stable strategy, the most extraordinary advantage is that this algorithm can carry through whole space research and will not get into local extremum, even under the case that fitness fuction is discontinuous, irregular and noise contaminated. This thesis proposes an evoked potential extraction method based on independent component analysis and genetic algorithm. We first take kurtosis of EP signal as datum mark and take reciprocal of the bias degree beween the kurtosis of other sigal and the datum mark as the fitness function. Then we extract the EP signal by searching the maximum of the fitness function through the whole space, and accomplish fast extraction of evoked potential. Since this method incorporate genetic algorith, it has strong astringency, When comparing to other EP signal extraction method it has many advantages such as fast computing, easily realized, not necessarily to introduce into the reference signals and so on.Since the brain signal collected clinically has abundant frenqency component, it is difficult to seprate them from each other completely only by ICA method, usually there are still much noise left in high frequency of the seprated signal. Considering the advantage of removal of noise by wavelet transform, if we do filter the noise from the mixing signal before we do further signal processing by ICA method, the seprated result is improved evidently. That can overcome shortcomings of present methods on noise undistinguishable. This thesis incorporates wavelet transform with ICA method based on genetic algorithm, and applicates them to evoked potentional extraction, we get satisfactory results.更多还原