【作者】 孙艳萍；

【导师】 吴清；

【作者基本信息】 河北工业大学 ， 模式识别与智能系统， 2007， 硕士

【摘要】 从头皮电极记录到的脑电信号,是脑内神经细胞电生理活动在大脑皮层或头皮表面的总体反映,其中包含了大量的生理与疾病信息,是一种无损伤性的成像技术。脑电对神经生理与神经心理的研究意义重大,对人类最终认识自身的神经活动规律也将产生重要影响。对脑电正、逆问题的研究是脑电问题中一种重要的研究方法,而源模型是求解脑电逆问题的关键因素之一,模型是对脑电活动源的估计,因此,该模型与真实神经元活动源的接近程度决定了脑电源定位的精确程度。本文主要完成了以下工作:1.在正问题方面,本文主要讨论并构建了产生脑电头皮电势的各类偶极子源模型——单偶极子源模型、双偶极源模型、三偶极源模型、线性偶极子源模型以及圆盘偶极子源模型。同时,针对各类偶极子模型,根据脑电正演理论,分别产生了不同头皮电势图,并根据头皮电极的位置进行了数据采样。2.本文分别利用支持向量机算法和BP算法设计分类器,根据脑电正问题产生的仿真数据对脑电源模型进行分类。试验结果表明:虽然BP算法在对大量测试样本进行分类时的错误率略低于支持向量机算法,但随着测试样本数量的减少,分类错误率也在不断升高,并且其训练速度也大大低于支持向量机算法。3.最后,本文结合脑电正问题相关算法和支持向量机分类器,设计实现了一个脑电源模型及其分类的仿真应用平台。应用该仿真平台可对脑电源模型进行正确且有效的分类,利用分类所获得的脑电源模型,可使得偶极子定位更为准确,更加接近真实脑电活动源。更多还原

【Abstract】 Electroencephalogram (EEG) is a set of electrical signals recorded from the scalp, it is the reflection of the nerve cells’electric phenomena in the pallium or scalp, it contains a lot of information about physiology and diseases. It is a scatheless imaging technology. EEG has very important research meanings to neurophysiology and neurology, and it will produce an effect on people knowing their own rules about neural movement.Study about the EEG forward problem and inverse problem is one of the research techniques about the EEG problem. Source model is the key to resolve the inverse problem and is the estimation of the neural current source. Therefore, the model and the real neural source’s degree of similitude decides the degree of accuracy about the location of the EEG source.The main works of this paper include:1. About the forward problem, this paper discussed and constructed some dipole source models which induce the potential on the scalp: one dipole source model, two dipole source model, three dipole source model, linear dipole source model and disc dipole source model. And then, we produced different scalp potential graphs of every dipole source models based on the forward problem theory, and then sampled the scalp potential data.2. In this paper, we designed two classifiers by used the support vector machine and BP algorithm, and we classified the simulated data produced by the forward problem theory. Testing result shows: although BP algorithm’s error rate is lower than support vector machine’s, its training speed is lower than the support vector machine’s, and the error rate is getting higher when the testing data becoming smaller.3. In the end, this paper designed and carried out a simulating application platform of EEG source model and classification combining the support vector machine algorithm and The EEG forward problem theory. We can get the EEG source model by using this platform, and it will help us locating the dipole more exactly, and more close to the real EEG source.更多还原