Machine Learning Interpretability Methods to Characterize Brain Network Dynamics in Epilepsy.
- DOI
- 10.1101/2023.06.25.23291874
- Published
- 2023 Oct 19
- Container
- medRxiv : the preprint server for health sciences
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1101/2023.06.25.23291874,
title = {Machine Learning Interpretability Methods to Characterize Brain Network Dynamics in Epilepsy.},
author = {Upadhyaya DP and Prantzalos K and Thyagaraj S and Shafiabadi N and Fernandez-BacaVaca G and Sivagnanam S and Majumdar A and Sahoo SS},
year = {2023},
journal = {medRxiv : the preprint server for health sciences},
doi = {10.1101/2023.06.25.23291874},
url = {https://doi.org/10.1101/2023.06.25.23291874}
}RIS
TY - JOUR TI - Machine Learning Interpretability Methods to Characterize Brain Network Dynamics in Epilepsy. AU - Upadhyaya DP AU - Prantzalos K AU - Thyagaraj S AU - Shafiabadi N AU - Fernandez-BacaVaca G AU - Sivagnanam S AU - Majumdar A AU - Sahoo SS PY - 2023 JO - medRxiv : the preprint server for health sciences DO - 10.1101/2023.06.25.23291874 UR - https://doi.org/10.1101/2023.06.25.23291874 ER -
APA
DP, U., K, P., S, T., N, S., G, F., S, S., A, M., & SS, S. (2023). Machine Learning Interpretability Methods to Characterize Brain Network Dynamics in Epilepsy.. medRxiv : the preprint server for health sciences. https://doi.org/10.1101/2023.06.25.23291874
Source records
- pubmed · retrieved 2026-09-26T00:33:40.991Z