Porr, B. , Daryanavard, S., Muñoz Bohollo, L., Cowan, H. and Dahiya, R. (2022) Real-time noise cancellation with deep learning. PLoS ONE, 17(11), e0277974. (doi: 10.1371/journal.pone.0277974) (PMID:36409690) (PMCID:PMC9678292)
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Abstract
Biological measurements are often contaminated with large amounts of non-stationary noise which require effective noise reduction techniques. We present a new real-time deep learning algorithm which produces adaptively a signal opposing the noise so that destructive interference occurs. As a proof of concept, we demonstrate the algorithm’s performance by reducing electromyogram noise in electroencephalograms with the usage of a custom, flexible, 3D-printed, compound electrode. With this setup, an average of 4dB and a maximum of 10dB improvement of the signal-to-noise ratio of the EEG was achieved by removing wide band muscle noise. This concept has the potential to not only adaptively improve the signal-to-noise ratio of EEG but can be applied to a wide range of biological, industrial and consumer applications such as industrial sensing or noise cancelling headphones.
| Item Type: | Articles |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Dahiya, Professor Ravinder and Porr, Dr Bernd and Munoz Bohollo, Miss Lucia and Daryanavard, Sama |
| Creator Roles: | Porr, B.Conceptualization, Investigation, Methodology, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review and editing Daryanavard, S.Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review and editing Muñoz Bohollo, L.Data curation, Investigation, Writing – review and editing Dahiya, R.Conceptualization, Funding acquisition, Writing – review and editing |
| Authors: | Porr, B., Daryanavard, S., Muñoz Bohollo, L., Cowan, H., and Dahiya, R. |
| College/School: | College of Science and Engineering > School of Engineering College of Science and Engineering > School of Engineering > Biomedical Engineering College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering |
| Journal Name: | PLoS ONE |
| Publisher: | Public Library of Science |
| ISSN: | 1932-6203 |
| ISSN (Online): | 1932-6203 |
| Copyright Holders: | Copyright © 2022 Porr et al. |
| First Published: | First published in PLoS ONE 17(11): e0277974 |
| Publisher Policy: | Reproduced under a Creative Commons License |
| Related URLs: | |
| Data DOI: | 10.5525/gla.researchdata.1258 |
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