ArticlePloS one2022
Real-time noise cancellation with deep learning.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Recent Progress in In-Ear EEG Technology and Its Emerging Real-World Applications: A Review.Micromachines · 2026Review
- Integration of Artificial Intelligence in Biosensors for Enhanced Detection of Foodborne Pathogens.Biosensors · 2025Review
- The Next Frontier in Brain Monitoring: A Comprehensive Look at In-Ear EEG Electrodes and Their Applications.Sensors (Basel, Switzerland) · 2025Review
- Multi-gate neuron-like transistors based on ensembles of aligned nanowires on flexible substrates.Nano convergence · 2025Article
- Enhanced diagnostics for generalized anxiety disorder: leveraging differential channel and functional connectivity features based on frontal EEG signals.Scientific reports · 2024Article
- Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities.Diagnostics (Basel, Switzerland) · 2024Review
- Real-time noise cancellation with deep learning.PloS one · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.