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Robin’s Viewer: Using deep-learning predictions to assist EEG annotation
Machine learning techniques such as deep learning have been increasingly used to assist EEG annotation, by automating artifact recognition, sleep staging, and seizure detection. In lack of automation, the annotation process is prone to bias, even for trained annotators. On the other hand, completely...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9951202/ https://www.ncbi.nlm.nih.gov/pubmed/36844437 http://dx.doi.org/10.3389/fninf.2022.1025847 |
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author | Weiler, Robin Diachenko, Marina Juarez-Martinez, Erika L. Avramiea, Arthur-Ervin Bloem, Peter Linkenkaer-Hansen, Klaus |
author_facet | Weiler, Robin Diachenko, Marina Juarez-Martinez, Erika L. Avramiea, Arthur-Ervin Bloem, Peter Linkenkaer-Hansen, Klaus |
author_sort | Weiler, Robin |
collection | PubMed |
description | Machine learning techniques such as deep learning have been increasingly used to assist EEG annotation, by automating artifact recognition, sleep staging, and seizure detection. In lack of automation, the annotation process is prone to bias, even for trained annotators. On the other hand, completely automated processes do not offer the users the opportunity to inspect the models’ output and re-evaluate potential false predictions. As a first step toward addressing these challenges, we developed Robin’s Viewer (RV), a Python-based EEG viewer for annotating time-series EEG data. The key feature distinguishing RV from existing EEG viewers is the visualization of output predictions of deep-learning models trained to recognize patterns in EEG data. RV was developed on top of the plotting library Plotly, the app-building framework Dash, and the popular M/EEG analysis toolbox MNE. It is an open-source, platform-independent, interactive web application, which supports common EEG-file formats to facilitate easy integration with other EEG toolboxes. RV includes common features of other EEG viewers, e.g., a view-slider, tools for marking bad channels and transient artifacts, and customizable preprocessing. Altogether, RV is an EEG viewer that combines the predictive power of deep-learning models and the knowledge of scientists and clinicians to optimize EEG annotation. With the training of new deep-learning models, RV could be developed to detect clinical patterns other than artifacts, for example sleep stages and EEG abnormalities. |
format | Online Article Text |
id | pubmed-9951202 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-99512022023-02-25 Robin’s Viewer: Using deep-learning predictions to assist EEG annotation Weiler, Robin Diachenko, Marina Juarez-Martinez, Erika L. Avramiea, Arthur-Ervin Bloem, Peter Linkenkaer-Hansen, Klaus Front Neuroinform Neuroinformatics Machine learning techniques such as deep learning have been increasingly used to assist EEG annotation, by automating artifact recognition, sleep staging, and seizure detection. In lack of automation, the annotation process is prone to bias, even for trained annotators. On the other hand, completely automated processes do not offer the users the opportunity to inspect the models’ output and re-evaluate potential false predictions. As a first step toward addressing these challenges, we developed Robin’s Viewer (RV), a Python-based EEG viewer for annotating time-series EEG data. The key feature distinguishing RV from existing EEG viewers is the visualization of output predictions of deep-learning models trained to recognize patterns in EEG data. RV was developed on top of the plotting library Plotly, the app-building framework Dash, and the popular M/EEG analysis toolbox MNE. It is an open-source, platform-independent, interactive web application, which supports common EEG-file formats to facilitate easy integration with other EEG toolboxes. RV includes common features of other EEG viewers, e.g., a view-slider, tools for marking bad channels and transient artifacts, and customizable preprocessing. Altogether, RV is an EEG viewer that combines the predictive power of deep-learning models and the knowledge of scientists and clinicians to optimize EEG annotation. With the training of new deep-learning models, RV could be developed to detect clinical patterns other than artifacts, for example sleep stages and EEG abnormalities. Frontiers Media S.A. 2023-01-19 /pmc/articles/PMC9951202/ /pubmed/36844437 http://dx.doi.org/10.3389/fninf.2022.1025847 Text en Copyright © 2023 Weiler, Diachenko, Juarez-Martinez, Avramiea, Bloem and Linkenkaer-Hansen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroinformatics Weiler, Robin Diachenko, Marina Juarez-Martinez, Erika L. Avramiea, Arthur-Ervin Bloem, Peter Linkenkaer-Hansen, Klaus Robin’s Viewer: Using deep-learning predictions to assist EEG annotation |
title | Robin’s Viewer: Using deep-learning predictions to assist EEG annotation |
title_full | Robin’s Viewer: Using deep-learning predictions to assist EEG annotation |
title_fullStr | Robin’s Viewer: Using deep-learning predictions to assist EEG annotation |
title_full_unstemmed | Robin’s Viewer: Using deep-learning predictions to assist EEG annotation |
title_short | Robin’s Viewer: Using deep-learning predictions to assist EEG annotation |
title_sort | robin’s viewer: using deep-learning predictions to assist eeg annotation |
topic | Neuroinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9951202/ https://www.ncbi.nlm.nih.gov/pubmed/36844437 http://dx.doi.org/10.3389/fninf.2022.1025847 |
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