动态脑磁逆问题研究
文献类型：学位论文
| 作者 | 刘婷 |
| 学位类别 | 硕士 |
| 答辩日期 | 2015-10 |
| 授予单位 | 中国科学院大学 |
| 导师 | 高欣 |
| 关键词 | 脑磁 动态逆问题 双正则化 奇异值分解 神经传递 |
| 其他题名 | Study of the Dynamic Magnetoencephalography Inverse Problem |
| 学位专业 | 机械电子工程 |
| 中文摘要 | 脑内神经信号的动态传递过程在临床神经疾病诊断和认知科学中得到越来越多的重视和研究。脑磁功能成像技术除了考量脑磁信号与脑磁源信号在空间上的映射关系，还要研究它们在时域上的关系。为求解动态脑磁逆问题，论文基于广泛使用的最小范数估计法，提出求解动态脑磁逆问题的两种解决方案。 双正则化技术已在心电逆问题、医学图像重建和电导率反演成像等诸多领域中被广泛研究，论文提出双正则化技术求解动态脑磁逆问题的可能性，改进广义交叉验证准则，通过遗传算法选取双正则化参数，然后找到有效的求解方法。实验结果表明，双正则化方法相比传统的最小范数估计法，重建的源信号均方误差更小，与仿真信号变化趋势更加吻合。 由于脑磁测量信号可看作是脑内源信号的线性组合，尝试从脑磁信号中分解出与源信号共同的时域信息，论文提出通过奇异值分解构造时序空间，将脑磁信号投影到时序空间进行逆问题求解，获得最小L2范数的动态脑磁逆问题解。奇异值分解方法的适用范围更广泛，包括癫痫棘波这样的尖峰信号。与双正则化方法相比，奇异值方法的计算量大大降低，不同噪声水平下奇异值分解方法的均方误差都小于双正则化方法，而且重建出的源信号与仿真信号吻合更佳，信噪比更高。 |
| 英文摘要 | There are more and more researches into the dynamic process of the brain neuronal populations in the field of clinical diagnosis and cognitive science. Magnetoencephalography（MEG）funcitonal imaging estimate the amplitude of all possible source locations. When imaging the dynamic process, the relationship between MEG data and source signals in space as well as in time domain should be studied. Based on the widely used minimum norm estimate(MNE), we proposed two methods to solve the dynamic MEG inverse problem. Two-parameter regularization has been adopted in researches on kinds of fields, such as electrocardiograph(ECG) inverse problem, medical image reconstruction，electrical conductivity imaging. We utilized two-parameter regularization to solve the dynamic MEG inverse problem and study its feasibility. Two tuning parameters were selected based on the generalized cross-validation criterion (GCV) and it was implement by the generic agrithorm(GA). An efficient solver was found and the inverse solutions were obtained as the linear combination of the one-parameter regularized solutions. Compared with MNE, the proposed method utilizing two-parameter regularization can get smaller overall mean squared error (MSE), and can reconstruct the shape of the time course of source better. We employed the singular value decomposition(SVD) of the sensor data to gain the temporal characteristics. Specifically, the MEG data was assumed to be linear combinations of the source signals, the singular value decomposition(SVD) of the sensor data was implemented and the the temporal subspace of source was defined by a set of the right singular vector. Then we got a new linear equation by projecting the sensor data and the source signals onto the temporal subspace. The dynamic inverse solutions were obtained by projecting the estimate to the new equation onto the solution space. Compared with the dual-parameter regularization method employing temporal smoothness constraint, the proposed method based on SVD of MEG data was easier to implement and the computation was much less. At different noise levels of the sensor data, this method can get smaller overall MSE and can reconstruct the shape of the time course of source better at a higer signal to noise ratio(SNR). |
| 语种 | 中文 |
| 公开日期 | 2016-05-03 |
| 源URL | [http://ir.ciomp.ac.cn/handle/181722/49256]  |
| 专题 | 长春光学精密机械与物理研究所_中科院长春光机所知识产出 |
| 推荐引用方式 GB/T 7714 | 刘婷. 动态脑磁逆问题研究[D]. 中国科学院大学. 2015. |
入库方式： OAI收割
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