Time-Delay Neural Networks and Independent Component Analysis for EEG-Based Prediction of Epileptic Seizures Propagation
Abstract
This research focuses on the development of a machine learning technique based on Time-Delay Neural Networks (TDNN) and Independent Component Analysis (ICA), to analyze EEG signal dynamics related to the initiation and propagation of epileptic seizures. We aim at designing a generative model to simulate EEG time-series after alteration of specific localized channels (electrodes) in order to explore the effects of brain surgery ex-vivo.
Cite
Text
Mirowski et al. "Time-Delay Neural Networks and Independent Component Analysis for EEG-Based Prediction of Epileptic Seizures Propagation." AAAI Conference on Artificial Intelligence, 2007. doi:10.1126/science.311.5763.934bMarkdown
[Mirowski et al. "Time-Delay Neural Networks and Independent Component Analysis for EEG-Based Prediction of Epileptic Seizures Propagation." AAAI Conference on Artificial Intelligence, 2007.](https://mlanthology.org/aaai/2007/mirowski2007aaai-time/) doi:10.1126/science.311.5763.934bBibTeX
@inproceedings{mirowski2007aaai-time,
title = {{Time-Delay Neural Networks and Independent Component Analysis for EEG-Based Prediction of Epileptic Seizures Propagation}},
author = {Mirowski, Piotr W. and Madhavan, Deepak and LeCun, Yann},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2007},
pages = {1892-1893},
doi = {10.1126/science.311.5763.934b},
url = {https://mlanthology.org/aaai/2007/mirowski2007aaai-time/}
}