ABOT: an open-source online benchmarking tool for machine learning-based artefact detection and removal methods from
Marcos Fabietti1, Mufti Mahmud2,3,4, Ahmad Lotfi1
1Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK.
This article introduces an open-source online platform designed to help researchers compare different machine learning methods for identifying and cleaning unwanted noise, or artefacts, from brain signal recordings. By organizing data from over 120 scientific studies into an interactive database, the tool simplifies the process of selecting the best signal-processing technique for specific experimental needs.
Area of Science:
- Neuroscience and ABOT benchmarking within computational biology
- Machine learning applications in neurophysiology
Background:
Brain signal acquisition remains a cornerstone for investigating neural activity and managing various neurological conditions. Unfortunately, extraneous noise often corrupts these recordings, complicating the interpretation of underlying biological phenomena. Such disturbances, commonly referred to as artefacts, frequently obscure critical data patterns. Researchers must eliminate these unwanted signals to ensure reliable analysis and accurate clinical conclusions. Computational approaches have emerged as robust solutions for managing these complex signal contaminations. Machine learning strategies have gained significant traction for their effectiveness in identifying and mitigating these issues. That uncertainty drove the need for a systematic way to navigate the rapidly expanding landscape of available algorithms. No prior work had resolved the difficulty of comparing diverse methodologies published across numerous disparate studies.
Purpose Of The Study:
The primary aim of this project is to introduce a specialized online platform for evaluating machine learning-based artefact removal techniques. This gap motivated the creation of a centralized resource to assist researchers in navigating the vast literature. The authors sought to address the challenges associated with finding and selecting the most appropriate algorithms for specific experiments. They intended to simplify the comparison process by compiling key characteristics into a structured knowledgebase. This effort focuses on reducing the time and effort required for manual literature reviews. The team designed the interface to be accessible, allowing users to search through numerous methods using various criteria. They aimed to promote better decision-making by providing clear, interactive data visualizations. This initiative strives to enhance the overall quality of signal processing in neurophysiological research.
Main Methods:
The review approach involved a comprehensive survey of over 120 peer-reviewed articles focusing on signal processing. Researchers extracted specific technical characteristics from each publication to populate the centralized database. They designed an interactive web interface to host these compiled findings for public access. The development team prioritized user-friendly navigation by incorporating dynamic tables and visual plots. They implemented filtering criteria to allow precise searching across various algorithmic parameters. The project team utilized open-source frameworks to ensure the software remained adaptable for future updates. They documented all underlying code to support transparency and facilitate community contributions. This systematic design ensures that the platform adheres to established standards for digital research infrastructure.
Main Results:
Key findings from the literature indicate that machine learning models vary significantly in their approach to signal cleaning. The database successfully aggregates information from more than 120 distinct scientific reports. These entries provide a standardized view of diverse detection and removal techniques. The interactive interface allows users to compare models based on multiple predefined search parameters. This organization reveals the breadth of current computational strategies for managing neural noise. The results show that the platform effectively bridges the gap between raw literature and practical application. Users can identify suitable methods more efficiently than through traditional manual review processes. The compiled data demonstrates the feasibility of creating a centralized resource for complex signal processing tasks.
Conclusions:
The authors demonstrate that their platform provides a structured environment for evaluating diverse signal-processing algorithms. This resource synthesizes information from over 120 distinct publications to support informed decision-making. By adhering to open-access standards, the project promotes transparency and reproducibility within the neuroscientific community. Users can leverage the interactive interface to filter methodologies based on specific experimental requirements. The researchers suggest that this centralized repository reduces the burden of manual literature reviews for signal processing. Their work confirms that organizing existing knowledge facilitates the selection of appropriate computational tools. The implementation of FAIR principles ensures that these resources remain accessible for future scientific endeavors. This synthesis highlights the utility of standardized benchmarking for advancing neurophysiological data analysis.
Frequently Asked Questions
The platform functions by hosting a curated knowledgebase of over 120 studies, allowing users to filter and compare machine learning models through an interactive interface featuring tables and plots. This approach contrasts with manual literature searches, which are often time-consuming and prone to selection bias.
The knowledgebase serves as the primary component, housing detailed characteristics of various algorithms. Unlike static review papers, this dynamic repository allows for real-time updates and user-driven queries based on specific performance criteria.
A user-friendly interface is necessary to manage the complexity of diverse machine learning models. The researchers propose that this design choice enables non-expert users to navigate technical data effectively, whereas command-line tools often require specialized programming knowledge.
The source code and documentation are hosted in an open-access repository to satisfy FAIR principles. This strategy ensures that the platform remains findable and reusable, distinguishing it from proprietary software that often restricts data transparency.
The system measures performance by aggregating key characteristics extracted from existing literature. The researchers propose that this comparative metric helps identify the most suitable model, unlike isolated studies that rarely provide standardized benchmarks across different datasets.
The authors propose that their benchmarking tool will streamline the selection process for researchers. They suggest that this resource will ultimately improve the reliability of signal decoding by reducing the time spent identifying effective noise-removal strategies.
