ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2024
Method for Synthetic Generation of LFP Data for Testing of Feature Extraction Algorithms
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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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