MEG Source Localization via Deep Learning

Sensors

Amir Adler¹  Dimitrios Pantazis¹ 

[1] McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;

关键词: magnetoencephalography;    deep learning;    source localization;    inverse problems;   

DOI  :  10.3390/s21134278

来源: DOAJ

【 摘 要 】

We present a deep learning solution to the problem of localization of magnetoencephalography (MEG) brain signals. The proposed deep model architectures are tuned to 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 resulting from head translation and rotation and a significant reduction in computation time, to a fraction of 1 ms, paving the way to real-time MEG source localization.

【 授权许可】

Unknown