ABOT: an open-source online benchmarking tool for machine learning-based artefact detection and removal methods from neuronal signals

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Fabietti, M ORCID: https://orcid.org/0000-0003-3093-5985, Mahmud, M ORCID: https://orcid.org/0000-0002-2037-8348, Lotfi, A ORCID: https://orcid.org/0000-0002-5139-6565 and Kaiser, MS, 2022. ABOT: an open-source online benchmarking tool for machine learning-based artefact detection and removal methods from neuronal signals. Brain Informatics, 9: 19. ISSN 2198-4018

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Official URL: https://doi.org/10.1186/s40708-022-00167-3

Abstract

Brain signals are recorded using different techniques to aid an accurate understanding of brain function and to treat its disorders. Untargeted internal and external sources contaminate the acquired signals during the recording process. Often termed as artefacts, these contaminations cause serious hindrances in decoding the recorded signals; hence, they must be removed to facilitate unbiased decision-making for a given investigation. Due to the complex and elusive manifestation of artefacts in neuronal signals, computational techniques serve as powerful tools for their detection and removal. Machine learning (ML) based methods have been successfully applied in this task. Due to ML’s popularity, many articles are published every year, making it challenging to find, compare and select the most appropriate method for a given experiment. To this end, this paper presents ABOT (Artefact removal Benchmarking Online Tool) as an online benchmarking tool which allows users to compare existing ML-driven artefact detection and removal methods from the literature. The characteristics and related information about the existing methods have been compiled as a knowledgebase (KB) and presented through a user-friendly interface with interactive plots and tables for users to search it using several criteria. Key characteristics extracted from over 120 articles from the literature have been used in the KB to help compare the specific ML models. To comply with the FAIR (Findable, Accessible, Interoperable and Reusable) principle, the source code and documentation of the toolbox have been made available via an open-access repository.

Item Type:	Journal article
Publication Title:	Brain Informatics
Creators:	Fabietti, M., Mahmud, M., Lotfi, A. and Kaiser, M.S.
Publisher:	Springer Science and Business Media LLC
Date:	December 2022
Volume:	9
ISSN:	2198-4018
Identifiers:	Number Type 10.1186/s40708-022-00167-3 DOI 1595172 Other
Rights:	© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Ward
Date Added:	06 Sep 2022 09:25
Last Modified:	06 Sep 2022 09:25
URI:	https://irep.ntu.ac.uk/id/eprint/46965