Machine Learning Interpretability Methods to Characterize Brain Network Dynamics in Epilepsy
- DOI
- 10.1101/2023.06.25.23291874
- Published
- 2023-06-28
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- Not recorded
- Publisher
- openRxiv
- Open access
- unknown
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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 = {Dipak P. Upadhyaya and Katrina Prantzalos and Suraj Thyagaraj and Nassim Shafiabadi and Guadalupe Fernandez-BacaVaca and Subhashini Sivagnanam and Amitava Majumdar and Satya S. Sahoo},
year = {2023},
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 - Dipak P. Upadhyaya AU - Katrina Prantzalos AU - Suraj Thyagaraj AU - Nassim Shafiabadi AU - Guadalupe Fernandez-BacaVaca AU - Subhashini Sivagnanam AU - Amitava Majumdar AU - Satya S. Sahoo PY - 2023 DO - 10.1101/2023.06.25.23291874 UR - https://doi.org/10.1101/2023.06.25.23291874 ER -
APA
Upadhyaya, D. P., Prantzalos, K., Thyagaraj, S., Shafiabadi, N., Fernandez-BacaVaca, G., Sivagnanam, S., Majumdar, A., & Sahoo, S. S. (2023). Machine Learning Interpretability Methods to Characterize Brain Network Dynamics in Epilepsy. https://doi.org/10.1101/2023.06.25.23291874
Source records
- crossref · retrieved 2026-11-10T11:46:12.313Z