| Clustering brain-network time series by Riemannian geometry K Slavakis, S Salsabilian, DS Wack, SF Muldoon, HE Baidoo-Williams, ... IEEE Transactions on Signal and Information Processing over Networks 4 (3 …, 2017 | 14 | 2017 |
| Quantifying changes in brain function following injury via network measures S Salsabilian, E Bibineyshvili, DJ Margolis, L Najafizadeh 2019 41st Annual International Conference of the IEEE Engineering in …, 2019 | 13 | 2019 |
| Using connectivity to infer behavior from cortical activity recorded through widefield transcranial imaging S Salsabilian, CR Lee, DJ Margolis, L Najafizadeh Optics and the Brain, BTu2C. 4, 2018 | 13 | 2018 |
| Study of functional network topology alterations after injury via embedding methods S Salsabilian, E Bibineyshvili, DJ Margolis, L Najafizadeh Optics and the Brain, BW4C. 3, 2020 | 10 | 2020 |
| Identifying task-related brain functional states via cortical networks S Salsabilian, L Zhu, CR Lee, DJ Margolis, L Najafizadeh 2020 IEEE International Symposium on Circuits and Systems (ISCAS), 1-4, 2020 | 7 | 2020 |
| Detection of mild traumatic brain injury via topological graph embedding and 2D convolutional neural networks S Salsabilian, L Najafizadeh 2020 42nd Annual International Conference of the IEEE Engineering in …, 2020 | 7 | 2020 |
| An adversarial variational autoencoder approach toward transfer learning for mTBI identification S Salsabilian, L Najafizadeh 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER …, 2021 | 4 | 2021 |
| Clustering time-varying connectivity networks by Riemannian geometry: The brain-network case K Slavakis, S Salsabilian, DS Wack, SF Muldoon 2016 IEEE Statistical Signal Processing Workshop (SSP), 1-5, 2016 | 4 | 2016 |
| Identifying mild traumatic brain injury using measures of frequency-specified networks S Salsabilian, Y Bibineyshvili, DJ Margolis, L Najafizadeh Journal of neural engineering 19 (5), 056033, 2022 | 3 | 2022 |
| A variational encoder framework for decoding behavior choices from neural data S Salsabilian, L Najafizadeh 2021 43rd Annual International Conference of the IEEE Engineering in …, 2021 | 3 | 2021 |
| Riemannian multi-manifold modeling and clustering in brain networks K Slavakis, S Salsabilian, DS Wack, SF Muldoon, HE Baidoo-Williams, ... Wavelets and Sparsity XVII 10394, 9-24, 2017 | 3 | 2017 |
| Clustering brain-network-connectivity states using kernel partial correlations K Slavakis, S Salsabilian, DS Wack, SF Muldoon, HE Baidoo-Williams, ... 2016 50th Asilomar Conference on Signals, Systems and Computers, 268-272, 2016 | 3 | 2016 |
| Subject-Invariant Feature Learning for mTBI Identification Using LSTM-based Variational Autoencoder with Adversarial Regularization S Salsabilian, L Najafizadeh Frontiers in Signal Processing, 69, 2022 | 1 | 2022 |
| An LSTM-based adversarial variational autoencoder framework for self-supervised neural decoding of behavioral choices S Salsabilian, C Lee, D Margolis, L Najafizadeh Journal of Neural Engineering, 2024 | | 2024 |
| Advanced computational analysis of neuroimaging data for brain injury identification and decoding behavior S Salsabilian Rutgers University-School of Graduate Studies, 2023 | | 2023 |
| Tracing functional network alternations following injury S Salsabilian, E Bibineyshvili, DJ Margolis, L Najafizadeh The Fifth Annual Rutgers Brain Health Institute (BHI) Symposium 2019, 25, 2019 | | 2019 |
| Riemannian-geometry-based modeling and clustering of network-wide non-stationary time series: The brain-network case K Slavakis, S Salsabilian, DS Wack, SF Muldoon, HE Baidoo-Williams, ... arXiv preprint arXiv:1701.07767, 2017 | | 2017 |
Laleh NajafizadehAssociate Professor of Electrical and Computer Engineering, Rutgers UniversityVerified email at ece.rutgers.edu
