{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/neuxus-a-biosignal-processing-and","title":"NeuXus: A Biosignal Processing and Classification Pipeline for Real-Time Brain-Computer Interaction","arxiv_id":"2012.12794","date":"2020-12-23","proceeding":null,"authors":["Athanasios Vourvopoulos","Simon Legeay","Patricia Figueiredo"],"abstract":"In the last few years,Brain-Computer Interfaces (BCIs) have progressed as an emerging research area in the fields of human-computer interaction and interactive systems.This is primarily due to the introduction of low-cost electroencephalographic (EEG) systems that render BCI technology accessible for non-medical research but also due to the advancements of signal processing and machine learning methods.Consequently,BCIs could provide a wide new range of possibilities in the way users interact with a computer system (e.g., neuroadaptive interfaces).However,major challenges must still be addressed for BCI systems to mature into an established communication medium for effective human-computer interaction. One of the major challenges involves the easy integration of real-time processing pipelines with portable EEG systems for an out-of-the-lab use. To date, despite the amount of options current open-source tools provide, most toolboxes focus mainly in extending the processing and classification methods but lack on the ability to provide an easy-to-design yet extensible architecture for ubiquitous use.Here, we present NeuXus, a modular toolbox in Python for real-time biosignal processing and pipeline design.NeuXus is open-source and platform independent,providing high-level implementation of processing pipelines for easy BCI design and deployment.","url_abs":"https://arxiv.org/abs/2012.12794v1","url_pdf":"https://arxiv.org/pdf/2012.12794v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"neuxus-a-biosignal-processing-and","repo_url":"https://github.com/LaSEEB/NeuXus","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}