The forward EEG solutions can be computed using artificial neural networks.
The forward EEG solutions can be computed using artificial neural networks.
复制标题
可以使用人工神经网络计算正向脑电图解。
DOI：
10.1109/10.855931
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发表时间：
2000
期刊：
影响因子：
--
通讯作者：
Sclabassi,RJ
中科院分区：
文献类型：
--
作者：
Sun,M;Sclabassi,RJ
脑电图（EEG）研究是基础脑研究和神经系统疾病临床诊断中最常用的方法之一。计算机和电子系统的最新技术进步使得可以通过大型电极阵列记录脑电图。使用头部体积导体模型对脑电波进行建模提供了一种定位大脑内功能发生器的有效方法。然而，由于边界元法 (BEM) 或有限元法 (FEM) 中使用的数值过程非常耗时，因此该模型的正向解（代表响应体积导体内电流源的理论电势）很难计算。本文提出了一种新颖的计算方法，使用人工神经网络（ANN）来映射前向解的两个向量。这两个向量对应于不同的头部模型，但是相对于相同的电流源。人工神经网络的输入向量基于球头模型，计算效率较高，但误差较大。人工神经网络的输出向量基于球体模型，该模型更加精确，但难以使用传统方法直接计算。作者的实验表明，这种ANN方法比BEM和FEM方法有了显着的改进：1）与精确解相比，计算均方误差仅约为0.3％； 2) 在线计算非常高效，每个通道仅需要 168 次浮点运算来计算前向解，并需要 10.2 k 字节的存储空间来表示整个 ANN。使用这种方法可以在个人计算机上准确地执行实时脑电图建模。
Study of electroencephalogarphy (EEG) is the one of the most utilized methods in both basic brain research and clinical diagnosis of neurological disorders. Recent technological advances in computer and electronic systems have allowed the EEG to be recorded from large electrode arrays. Modeling the brain waves using a head volume conductor model provides an effective method to localize functional generators within the brain. However, the forward solutions to this model, which represent theoretical potentials in response to current sources within the volume conductor, are difficult to compute because of time-consuming numerical procedures utilized in either the boundary element method (BEM) or the finite element method (FEM). This paper presents a novel computational approach using an artificial neural network (ANN) to map two vectors of forward solutions. These two vectors correspond to different head models but with respect to the same current source. The input vector to the ANN is based on the spherical head model, which can be computed efficiently but involves large errors. The output vector from the ANN is based on the spheroidal model, which is more precise, but difficult to compute directly using the traditional means. The authors' experiments indicate that this ANN approach provides a remarkable improvement over the BEM and FEM methods: 1) the mean-square error of computation was only approximately 0.3% compared to the exact solution; 2) the online computation was extremely efficient, requiring only 168 floating point operations per channel to compute the forward solution, and 10.2 k-bytes of storage to represent the entire ANN. Using this approach it is possible to perform real-time EEG modeling accurately on personal computers.