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.
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Different algorithms have been proposed for continuous phase estimation in real-time.
“although different algorithms have been proposed for continuous phase estimation in real-time (Kim et al., 2016; Blackwood et al., 2018)”
From Toward a personalized closed-loop stimulation of the visual cortex: Advances and challenges (2022) · cites this paper as Kim et al., 2016
Cites
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Speech recognition with deep recurrent neural networks
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Modeling the Spatial Reach of the LFP
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Cortical Local Field Potential Encodes Movement Intentions in the Posterior Parietal Cortex
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Beta oscillations in freely moving Parkinson's subjects are attenuated during deep brain stimulation
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Self-citationAnalysis of local field potential signals: A systems approach
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Toward a personalized closed-loop stimulation of the visual cortex: Advances and challenges
Grani, F., Soto-Sánchez, C., Fimia, A., & Fernández, E. (2022) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2022.1034270 · Cited by 16 · see citing works
- Authors
- Kim, L., Harer, J., Rangamani, A., Moran, J., Parks, P. D., Widge, A., Eskandar, E., Dougherty, D., & Chin, S. P.
- Affiliations
- Draper Laboratory (US)
- Johns Hopkins University (US)
- Massachusetts General Hospital (US)
- Venue
- 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- Publisher
- IEEE
- Published
- 2016-08
- Volume
- 2016
- Pages
- 808-811
- ISSN
- 2375-7477 (ISSN-L)
- OpenAlex
- W2534427682
- Cited by
- 8
- References
- 12
- Citations by year
- 2026: 1 · 2025: 1 · 2023: 1 · 2022: 2 · 2018: 3
- FWCI
- 1.72
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- local field potentials, LSTM, recurrent neural networks, epilepsy, time series forecasting
- MeSH terms
- Epilepsy, Humans, Sensitivity and Specificity, Databases, Factual, Neural Networks, Computer
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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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