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Deep Neural Imputation: A Framework for Recovering Incomplete Brain Recordings

Talukder, Sabera and Sun, Jennifer J. and Leonard, Matthew and Brunton, Bingni W. and Yue, Yisong (2022) Deep Neural Imputation: A Framework for Recovering Incomplete Brain Recordings. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20220714-212423144

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Abstract

Neuroscientists and neuroengineers have long relied on multielectrode neural recordings to study the brain. However, in a typical experiment, many factors corrupt neural recordings from individual electrodes, including electrical noise, movement artifacts, and faulty manufacturing. Currently, common practice is to discard these corrupted recordings, reducing already limited data that is difficult to collect. To address this challenge, we propose Deep Neural Imputation (DNI), a framework to recover missing values from electrodes by learning from data collected across spatial locations, days, and participants. We explore our framework with a linear nearest-neighbor approach and two deep generative autoencoders, demonstrating DNI's flexibility. One deep autoencoder models participants individually, while the other extends this architecture to model many participants jointly. We evaluate our models across 12 human participants implanted with multielectrode intracranial electrocorticography arrays; participants had no explicit task and behaved naturally across hundreds of recording hours. We show that DNI recovers not only time series but also frequency content, and further establish DNI's practical value by recovering significant performance on a scientifically-relevant downstream neural decoding task.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.48550/arXiv.2206.08094arXivDiscussion Paper
ORCID:
AuthorORCID
Sun, Jennifer J.0000-0002-0906-6589
Yue, Yisong0000-0001-9127-1989
Additional Information:Attribution 4.0 International (CC BY 4.0) We thank Albert Hao Li for thoughtful discussions and feedback throughout the project, Steve Peterson & Zoe Steine-Hanson for sharing their AJILE12 dataset knowledge, and Ann Kennedy for helpful conversations. This work was supported by an NSF Graduate Fellowship (to ST), NSERC Award #PGSD3-532647-2019 (to JJS), and the Moore Distinguished Scholar Program at Caltech (to BWB).
Funders:
Funding AgencyGrant Number
NSF Graduate Research FellowshipUNSPECIFIED
Natural Sciences and Engineering Research Council of Canada (NSERC)PGSD3-532647-2019
Gordon and Betty Moore FoundationUNSPECIFIED
Record Number:CaltechAUTHORS:20220714-212423144
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220714-212423144
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115570
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:15 Jul 2022 22:46
Last Modified:15 Jul 2022 22:46

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