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.
Original language | English (US) |
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Title of host publication | 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 808-811 |
Number of pages | 4 |
ISBN (Electronic) | 9781457702204 |
DOIs | |
State | Published - Oct 13 2016 |
Externally published | Yes |
Event | 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States Duration: Aug 16 2016 → Aug 20 2016 |
Publication series
Name | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS |
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Volume | 2016-October |
ISSN (Print) | 1557-170X |
Other
Other | 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 |
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Country/Territory | United States |
City | Orlando |
Period | 8/16/16 → 8/20/16 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.