A Toolbox and Crowdsourcing Platform for Automatic Labeling of Independent Components in Electroencephalography
Independent Component Analysis (ICA) is a conventional approach to exclude non-brain signals such as eye movements and muscle artifacts from electroencephalography (EEG). A rejection of independent components (ICs) is usually performed in semiautomatic mode and requires experts’ involvement. As also...
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Frontiers Media S.A.
2021
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oai:doaj.org-article:9d25f5e1329440c78e8fc7e2d97a4dd42021-12-02T14:24:06ZA Toolbox and Crowdsourcing Platform for Automatic Labeling of Independent Components in Electroencephalography1662-519610.3389/fninf.2021.720229https://doaj.org/article/9d25f5e1329440c78e8fc7e2d97a4dd42021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fninf.2021.720229/fullhttps://doaj.org/toc/1662-5196Independent Component Analysis (ICA) is a conventional approach to exclude non-brain signals such as eye movements and muscle artifacts from electroencephalography (EEG). A rejection of independent components (ICs) is usually performed in semiautomatic mode and requires experts’ involvement. As also revealed by our study, experts’ opinions about the nature of a component often disagree, highlighting the need to develop a robust and sustainable automatic system for EEG ICs classification. The current article presents a toolbox and crowdsourcing platform for Automatic Labeling of Independent Components in Electroencephalography (ALICE) available via link http://alice.adase.org/. The ALICE toolbox aims to build a sustainable algorithm to remove artifacts and find specific patterns in EEG signals using ICA decomposition based on accumulated experts’ knowledge. The difference from previous toolboxes is that the ALICE project will accumulate different benchmarks based on crowdsourced visual labeling of ICs collected from publicly available and in-house EEG recordings. The choice of labeling is based on the estimation of IC time-series, IC amplitude topography, and spectral power distribution. The platform allows supervised machine learning (ML) model training and re-training on available data subsamples for better performance in specific tasks (i.e., movement artifact detection in healthy or autistic children). Also, current research implements the novel strategy for consentient labeling of ICs by several experts. The provided baseline model could detect noisy IC and components related to the functional brain oscillations such as alpha and mu rhythm. The ALICE project implies the creation and constant replenishment of the IC database, which will improve ML algorithms for automatic labeling and extraction of non-brain signals from EEG. The toolbox and current dataset are open-source and freely available to the researcher community.Gurgen SoghoyanGurgen SoghoyanAlexander LedovskyAlexander LedovskyMaxim NekrashevichOlga MartynovaIrina PolikanovaGalina PortnovaAnna RebreikinaOlga SysoevaMaxim SharaevMaxim SharaevFrontiers Media S.A.articleEEGautomatic preprocessingICAchildrenautomatic artifact detectionmachine learning algorithmsNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neuroinformatics, Vol 15 (2021) |
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| language |
EN |
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EEG automatic preprocessing ICA children automatic artifact detection machine learning algorithms Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 |
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EEG automatic preprocessing ICA children automatic artifact detection machine learning algorithms Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Gurgen Soghoyan Gurgen Soghoyan Alexander Ledovsky Alexander Ledovsky Maxim Nekrashevich Olga Martynova Irina Polikanova Galina Portnova Anna Rebreikina Olga Sysoeva Maxim Sharaev Maxim Sharaev A Toolbox and Crowdsourcing Platform for Automatic Labeling of Independent Components in Electroencephalography |
| description |
Independent Component Analysis (ICA) is a conventional approach to exclude non-brain signals such as eye movements and muscle artifacts from electroencephalography (EEG). A rejection of independent components (ICs) is usually performed in semiautomatic mode and requires experts’ involvement. As also revealed by our study, experts’ opinions about the nature of a component often disagree, highlighting the need to develop a robust and sustainable automatic system for EEG ICs classification. The current article presents a toolbox and crowdsourcing platform for Automatic Labeling of Independent Components in Electroencephalography (ALICE) available via link http://alice.adase.org/. The ALICE toolbox aims to build a sustainable algorithm to remove artifacts and find specific patterns in EEG signals using ICA decomposition based on accumulated experts’ knowledge. The difference from previous toolboxes is that the ALICE project will accumulate different benchmarks based on crowdsourced visual labeling of ICs collected from publicly available and in-house EEG recordings. The choice of labeling is based on the estimation of IC time-series, IC amplitude topography, and spectral power distribution. The platform allows supervised machine learning (ML) model training and re-training on available data subsamples for better performance in specific tasks (i.e., movement artifact detection in healthy or autistic children). Also, current research implements the novel strategy for consentient labeling of ICs by several experts. The provided baseline model could detect noisy IC and components related to the functional brain oscillations such as alpha and mu rhythm. The ALICE project implies the creation and constant replenishment of the IC database, which will improve ML algorithms for automatic labeling and extraction of non-brain signals from EEG. The toolbox and current dataset are open-source and freely available to the researcher community. |
| format |
article |
| author |
Gurgen Soghoyan Gurgen Soghoyan Alexander Ledovsky Alexander Ledovsky Maxim Nekrashevich Olga Martynova Irina Polikanova Galina Portnova Anna Rebreikina Olga Sysoeva Maxim Sharaev Maxim Sharaev |
| author_facet |
Gurgen Soghoyan Gurgen Soghoyan Alexander Ledovsky Alexander Ledovsky Maxim Nekrashevich Olga Martynova Irina Polikanova Galina Portnova Anna Rebreikina Olga Sysoeva Maxim Sharaev Maxim Sharaev |
| author_sort |
Gurgen Soghoyan |
| title |
A Toolbox and Crowdsourcing Platform for Automatic Labeling of Independent Components in Electroencephalography |
| title_short |
A Toolbox and Crowdsourcing Platform for Automatic Labeling of Independent Components in Electroencephalography |
| title_full |
A Toolbox and Crowdsourcing Platform for Automatic Labeling of Independent Components in Electroencephalography |
| title_fullStr |
A Toolbox and Crowdsourcing Platform for Automatic Labeling of Independent Components in Electroencephalography |
| title_full_unstemmed |
A Toolbox and Crowdsourcing Platform for Automatic Labeling of Independent Components in Electroencephalography |
| title_sort |
toolbox and crowdsourcing platform for automatic labeling of independent components in electroencephalography |
| publisher |
Frontiers Media S.A. |
| publishDate |
2021 |
| url |
https://doaj.org/article/9d25f5e1329440c78e8fc7e2d97a4dd4 |
| work_keys_str_mv |
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