ArticleeNeuro
Improved Manual Annotation of EEG Signals through Convolutional Neural Network Guidance.
Article in eNeuro. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 11 citations in OpenAlex.
- Critical Dynamics in the Association Cortex Predict Higher Intelligence in Typically Developing Children.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Unsupervised clustering of pre-ictal EEG in children: a reproducible and lightweight CPU-based workflow.Frontiers in neurology · 2025Article
- Reliable and fast automatic artifact rejection of Long-Term EEG recordings based on Isolation Forest.Medical & biological engineering & computing · 2024Article
- Robin's Viewer: Using deep-learning predictions to assist EEG annotation.Frontiers in neuroinformatics · 2022Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of validated algorithms for automated handling of artifacts is essential for reliable and fast processing of EEG signals. Recently, there have been methodological advances in designing machine-learning algorithms to improve artifact detection of trained professionals who usually meticulously inspect and manually annotate EEG signals. However, validation of these methods is hindered by the lack of a gold standard as data are mostly private and data annotation is time consuming and error prone. In the effort to circumvent these issues, we propose an iterative learning model to speed up and reduce errors of manual annotation of EEG. We use a convolutional neural network (CNN) to train on expert-annotated eyes-open and eyes-closed resting-state EEG data from typically developing children (
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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.