EEG Source Localization Using Spatio-Temporal Neural Network
EEG Source Localization Using Spatio-Temporal Neural Network
复制标题
使用时空神经网络进行脑电图源定位
DOI：
10.23919/j.cc.2019.07.011
复制
发表时间：
2019-07-01
影响因子：
4.1
通讯作者：
Wang, Changming
中科院分区：
文献类型：
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作者：
Cui, Song;Duan, Lijuan;Wang, Changming
头皮脑电图（sEEG）信号的病灶电活动源定位通常被建模为一个高度不适定的逆问题。本文提出了一种基于时空长短期记忆递归神经网络（LSTM）的脑电逆问题源定位方法。网络模型由sEEG编码和源解码两部分组成，对sEEG信号进行建模并接收源位置回归。由于没有足够的带注释的sEEG信号对应于特定的源位置，因此采用有限元法（FEM）正演模型生成模拟数据，作为训练信号的一部分。提出了一种基于模拟训练数据估计源位置的源定位框架。在模拟测试数据上进行了实验。仿真结果表明，该网络对噪声信号具有较好的鲁棒性，解决了基于时空深度网络的脑电反演问题。结果表明，该方法克服了数据驱动学习的高度不适定线性逆问题。
Source localization of focal electrical activity from scalp electroencephalogram (sEEG) signal is generally modeled as an inverse problem that is highly ill-posed. In this paper, a novel source localization method is proposed to model the EEG inverse problem using spatio-temporal long-short term memory recurrent neural networks (LSTM). The network model consists of two parts, sEEG encoding and source decoding, to model the sEEG signal and receive the regression of source location. As there does not exist enough annotated sEEG signals correspond to specific source locations, simulated data is generated with forward model using finite element method (FEM) to act as a part of training signals. A framework for source localization is proposed to estimate the source position based on simulated training data. Experiments are done on simulated testing data. The results on simulated data exhibit good robustness on noise signal, and the proposed network solves the EEG inverse problem with spatio-temporal deep network. The result show that the proposed method overcomes the highly ill-posed linear inverse problem with data driven learning.