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.

中文翻译：

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ABOT：一种开源在线基准测试工具，用于从神经元信号中进行基于机器学习的伪影检测和去除方法

使用不同的技术记录大脑信号，以帮助准确了解大脑功能并治疗其疾病。在记录过程中，非目标内部和外部源会污染采集到的信号。这些污染通常被称为人工制品，会严重阻碍记录信号的解码；因此，必须删除它们以促进对给定调查的公正决策。由于神经元信号中伪影的复杂和难以捉摸的表现，计算技术可作为检测和移除伪影的强大工具。基于机器学习 (ML) 的方法已成功应用于此任务。由于 ML 的流行，每年都会发表许多文章，这使得为给定实验寻找、比较和选择最合适的方法变得具有挑战性。为此，本文介绍了 ABOT（Artefact removal Benchmarking Online Tool）作为一种在线基准测试工具，它允许用户从文献中比较现有的 ML 驱动的人工制品检测和去除方法。有关现有方法的特征和相关信息已被编译为知识库 (KB)，并通过带有交互式图表和表格的用户友好界面呈现，供用户使用多种标准进行搜索。知识库中使用从 120 多篇文献中提取的关键特征来帮助比较特定的 ML 模型。为了遵守 FAIR（可查找、可访问、可互操作和可重用）原则，工具箱的源代码和文档已通过开放访问存储库提供。