Loading...
Please wait, while we are loading the content...
Similar Documents
MEG Source Localization via Deep Learning
| Content Provider | MDPI |
|---|---|
| Author | Pantazis, Dimitrios Adler, Amir |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 4278 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s21134278 |
| Journal | Sensors |
| Issue Number | 13 |
| Volume Number | 21 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-06-22 |
| Access Restriction | Open |
| Subject Keyword | Sensors Artificial Intelligence Magnetoencephalography Deep Learning Source Localization Inverse Problems |
| Content Type | Text |
| Resource Type | Article |
