Optimizing Neural Data Analysis: Determining Minimum Recording Length for Unambigous Signal Processing

  • Advanced silicon electrode arrays facilitate the recording of thousands of neurons, generating extensive neural data that imposes a significant burden on researchers and processing algorithms. Thus, real-time analysis pipelines are gaining increasing value, while at the same time having to deal with non-stationary and noisy data. We intend to apply Machine Learning (ML) algorithms to a dense setAdvanced silicon electrode arrays facilitate the recording of thousands of neurons, generating extensive neural data that imposes a significant burden on researchers and processing algorithms. Thus, real-time analysis pipelines are gaining increasing value, while at the same time having to deal with non-stationary and noisy data. We intend to apply Machine Learning (ML) algorithms to a dense set of recordings from rat brains in order to prepare a functional atlas, correlating neuronal signals with anatomical position. While doing so, we needed to decide on a rational way which recording snippet length would best represent the original, longer source sequence and thus suffices to be further processed for anatomical correlation. We implemented an algorithm to evaluate the spectral information of systematically length varied records based on similarity to the original record. For our dataset a recording duration of 3 seconds satisfied moderate requirements across all channels, thus allowing us to reduce computational load for ongoing ML classification of microprobe sourced electrophysiologic signals.show moreshow less

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Metadaten
Author:Ali AmanpourORCiD, Timo BaumannORCiDGND, Ulrich G. HofmannORCiD
URN:urn:nbn:de:bvb:898-opus4-80606
DOI:https://doi.org/10.35096/othr/pub-8060
Document Type:Preprint
Language:English
Year of first Publication:2025
Publishing Institution:Ostbayerische Technische Hochschule Regensburg
Release Date:2025/05/16
Pagenumber:5
Konferenzangabe:2025 47th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), July 14-17, 2025, Copenhagen, Denmark
Institutes:Fakultät Informatik und Mathematik
Begutachtungsstatus:peer-reviewed
research focus:Gesundheit und Soziales
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG
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