Repairing Artifacts in Neural Activity Recordings Using Low-Rank Matrix Estimation

Electrophysiology recordings are frequently affected by artifacts (e.g., subject motion or eye movements), which reduces the number of available trials and affects the statistical power. When artifacts are unavoidable and data are scarce, signal reconstruction algorithms that allow for the retention...

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Detalles Bibliográficos
Autores principales: Naik, Shruti, Dehaene-Lambertz, Ghislaine, Battaglia, Demian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10220667/
https://www.ncbi.nlm.nih.gov/pubmed/37430760
http://dx.doi.org/10.3390/s23104847

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10220667/
https://www.ncbi.nlm.nih.gov/pubmed/37430760
http://dx.doi.org/10.3390/s23104847

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