Method for Synthetic Generation of LFP Data for Testing of Feature Extraction Algorithms

Heather J. Breidenbach; Virginia Woods; Uisub Shin; Evan Dastin Van Rijn; Mahsa Shoaran; Alik S. Widge

doi:10.1109/embc53108.2024.10781522

Method for Synthetic Generation of LFP Data for Testing of Feature Extraction Algorithms

Heather J. Breidenbach, Virginia Woods, Uisub Shin, Evan Dastin Van Rijn, Mahsa Shoaran, Alik S. Widge

Psychiatry & Behavioral Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Recent interest in closed-loop neuromodulation devices has driven development of algorithms capable of real-time biomarker extraction. Synthetic data for tuning algorithmic parameters in various oscillatory cases is a useful tool but must be generated to model realistic neural behavior. We extracted key oscillatory behaviors from rodent LFPs and used this information to create a realistic generation method for synthetic signal production. We then used the generated signals to optimize the feature extraction performance of a real-time feature extraction algorithm. The results of the algorithm testing closely mirrored results from testing on recorded neural LFPs and resembled this real data more closely than a simplistic model of synthetic neural data.

Original language	English (US)
Title of host publication	46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350371499
DOIs	https://doi.org/10.1109/embc53108.2024.10781522
State	Published - 2024
Event	46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Orlando, United States Duration: Jul 15 2024 → Jul 19 2024

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)	1557-170X

Conference

Conference	46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024
Country/Territory	United States
City	Orlando
Period	7/15/24 → 7/19/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

PubMed: MeSH publication types

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10.1109/embc53108.2024.10781522

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Breidenbach, H. J., Woods, V., Shin, U., Rijn, E. D. V., Shoaran, M., & Widge, A. S. (2024). Method for Synthetic Generation of LFP Data for Testing of Feature Extraction Algorithms. In 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Proceedings (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/embc53108.2024.10781522

Method for Synthetic Generation of LFP Data for Testing of Feature Extraction Algorithms. / Breidenbach, Heather J.; Woods, Virginia; Shin, Uisub et al.
46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2024. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Breidenbach, HJ, Woods, V, Shin, U, Rijn, EDV, Shoaran, M & Widge, AS 2024, Method for Synthetic Generation of LFP Data for Testing of Feature Extraction Algorithms. in 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Proceedings. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers Inc., 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024, Orlando, United States, 7/15/24. https://doi.org/10.1109/embc53108.2024.10781522

Breidenbach HJ, Woods V, Shin U, Rijn EDV, Shoaran M, Widge AS. Method for Synthetic Generation of LFP Data for Testing of Feature Extraction Algorithms. In 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2024. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/embc53108.2024.10781522

Breidenbach, Heather J. ; Woods, Virginia ; Shin, Uisub et al. / Method for Synthetic Generation of LFP Data for Testing of Feature Extraction Algorithms. 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2024. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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abstract = "Recent interest in closed-loop neuromodulation devices has driven development of algorithms capable of real-time biomarker extraction. Synthetic data for tuning algorithmic parameters in various oscillatory cases is a useful tool but must be generated to model realistic neural behavior. We extracted key oscillatory behaviors from rodent LFPs and used this information to create a realistic generation method for synthetic signal production. We then used the generated signals to optimize the feature extraction performance of a real-time feature extraction algorithm. The results of the algorithm testing closely mirrored results from testing on recorded neural LFPs and resembled this real data more closely than a simplistic model of synthetic neural data.",

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AU - Widge, Alik S.

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AB - Recent interest in closed-loop neuromodulation devices has driven development of algorithms capable of real-time biomarker extraction. Synthetic data for tuning algorithmic parameters in various oscillatory cases is a useful tool but must be generated to model realistic neural behavior. We extracted key oscillatory behaviors from rodent LFPs and used this information to create a realistic generation method for synthetic signal production. We then used the generated signals to optimize the feature extraction performance of a real-time feature extraction algorithm. The results of the algorithm testing closely mirrored results from testing on recorded neural LFPs and resembled this real data more closely than a simplistic model of synthetic neural data.

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Method for Synthetic Generation of LFP Data for Testing of Feature Extraction Algorithms

Abstract

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