MEG Source Localization Via Deep Learning
Name
sensors-21-04278.pdf
Size
1.83 MB
Format
Unknown
Checksum (MD5)
01aef255cabc7a00cd4d219a1b04189e
Author(s)
Pantazis, Dimitrios
Adler, Amir
Date Issued
June 22, 2021
Journal
Sensors
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Sensors 21 (13): 4278 (2021)
Version
Final published version
Abstract
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.
MIT Department
McGovern Institute for Brain Research at MIT
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3390/s21134278
