论文标题
使用深度加权的深度先验的当前源本地化
Current Source Localization Using Deep Prior with Depth Weighting
论文作者
论文摘要
本文提出了一种基于深处的新型神经元电流源定位方法，它代表了使用卷积网络对当前源的更为复杂的先验分布。已提出了深层的先验，作为一种无监督的学习方法的一种手段，该方法不需要使用培训数据学习，并且使用随机进行的神经网络使用单个观察结果来更新源位置。在我们以前的工作中，已经提出了一种基于深点的当前源定位方法，但性能与常规方法（例如Sloreta）的性能并不相同。为了改善基于深度的方法，在本文中，引入了当前源的深度重量，以进行深度先验，其中深度加权量相当于为浅表电流分配更多的惩罚。通过对模拟MEG数据进行当前源估计的实验证实了其有效性。
This paper proposes a novel neuronal current source localization method based on Deep Prior that represents a more complicated prior distribution of current source using convolutional networks. Deep Prior has been suggested as a means of an unsupervised learning approach that does not require learning using training data, and randomly-initialized neural networks are used to update a source location using a single observation. In our previous work, a Deep-Prior-based current source localization method in the brain has been proposed but the performance was not almost the same as those of conventional approaches, such as sLORETA. In order to improve the Deep-Prior-based approach, in this paper, a depth weight of the current source is introduced for Deep Prior, where depth weighting amounts to assigning more penalty to the superficial currents. Its effectiveness is confirmed by experiments of current source estimation on simulated MEG data.
