@inproceedings{4c1f6dff0f7341bda5ec3540be29a0db,
title = "Predicting local field potentials with recurrent neural networks",
abstract = "We present a Recurrent Neural Network using LSTM (Long Short Term Memory) that is capable of modeling and predicting Local Field Potentials. We train and test the network on real data recorded from epilepsy patients. We construct networks that predict multi-channel LFPs for 1, 10, and 100 milliseconds forward in time. Our results show that prediction using LSTM outperforms regression when predicting 10 and 100 millisecond forward in time.",
author = "Louis Kim and Jacob Harer and Akshay Rangamani and James Moran and Parks, {Philip D.} and Alik Widge and Emad Eskandar and Darin Dougherty and Chin, {Sang Peter}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 ; Conference date: 16-08-2016 Through 20-08-2016",
year = "2016",
month = oct,
day = "13",
doi = "10.1109/EMBC.2016.7590824",
language = "English (US)",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "808--811",
booktitle = "2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016",
address = "United States",
}