| Deep neural networks constrained by neural mass models improve electrophysiological source imaging of spatiotemporal brain dynamics R Sun, A Sohrabpour, GA Worrell, B He Proceedings of the National Academy of Sciences 119 (31), e2201128119, 2022 | 31 | 2022 |
| Sifnet: Electromagnetic source imaging framework using deep neural networks R Sun, A Sohrabpour, S Ye, B He bioRxiv, 2020.05. 11.089185, 2020 | 7 | 2020 |
| Deep learning based source imaging provides strong sublobar localization of epileptogenic zone from MEG interictal spikes R Sun, W Zhang, A Bagić, B He NeuroImage 281, 120366, 2023 | 1 | 2023 |
| Personalized Deep Learning based Source Imaging Framework Improves the Imaging of Epileptic Sources from MEG Interictal Spikes R Sun, W Zhang, A Bagić, B He bioRxiv, 2022.11. 13.516312, 2022 | 1 | 2022 |
| Methods and apparatus for electromagnetic source imaging using deep neural networks B He, R Sun, A Sohrabpour US Patent App. 17/315,691, 2021 | 1 | 2021 |
| Effects of EEG Electrode Numbers on Deep Learning-Based Source Imaging J Rong, R Sun, Y Guo, B He International Conference on Brain Informatics, 123-132, 2023 | | 2023 |
| Deep learning-based Source Imaging Improves Spatiotemporal Imaging of Epileptic Sources R Sun Carnegie Mellon University, 2023 | | 2023 |
| Spatiotemporal Rhythmic Seizure Sources Can be Imaged by means of Biophysically Constrained Deep Neural Networks R Sun, A Sohrabpour, B Joseph, G Worrell, B He medRxiv, 2023.11. 30.23299218, 2023 | | 2023 |
