论文标题
MEG通过深度学习来源本地化
MEG Source Localization via Deep Learning
论文作者
论文摘要
我们为磁脑摄影（MEG）脑信号的定位问题提供了深入的学习解决方案。提出的深层模型体系结构是针对单个和多个时间点MEG数据调整的，并且可以估计数量的偶极源。在实际人类受试者的皮质表面的模拟数据中，在具有不同SNR级别，源间相关值和来源数量的特定情况下，对流行的RAP-Music定位算法的模拟数据结果表明，对流行的RAP音乐定位算法有所改善。重要的是，深度学习模型具有强大的性能，可以转发模型误差和计算时间的显着减少，将其缩小到1 ms的一部分，为实时MEG源本地化铺平了道路。
We present a deep learning solution to the problem of localization of magnetoencephalography (MEG) brain signals. The proposed deep model architectures are tuned for single and multiple time point MEG data, and can estimate varying numbers of dipole sources. Results from simulated MEG data on the cortical surface of a real human subject demonstrated improvements against the popular RAP-MUSIC localization algorithm in specific scenarios with varying SNR levels, inter-source correlation values, and number of sources. Importantly, the deep learning models had robust performance to forward model errors and a significant reduction in computation time, to a fraction of 1 ms, paving the way to real-time MEG source localization.
