NeuXus：一种用于实时的脑机交互的生物信号处理和分类流水线
NeuXus: A Biosignal Processing and Classification Pipeline for Real-Time Brain-Computer Interaction
摘要：
在过去的几年里，脑机接口（BCI）作为人机交互和交互系统领域的一个新兴研究领域取得了进展。这主要是由于低成本脑电图（EEG）系统的引入，使BCI技术可用于非医学研究，也是由于信号处理和机器学习方法的进步。因此，BCI可以在用户与计算机系统交互的方式上提供广泛的新的可能性（例如，然而，对于BCI系统来说，仍然必须解决主要的挑战以成熟为用于有效的人机交互的已建立的通信介质。其中一个主要的挑战涉及到实时的【形】处理管道与便携式EEG系统的简单集成，以供实验室外使用。迄今为止，尽管当前开源工具提供了大量的选项，但大多数工具箱主要集中在扩展处理和分类方法上，但缺乏提供易于设计但可扩展的架构以供普遍使用的能力。在这里，我们介绍NeuXus，一个Python中的模块化工具箱，用于实时的【形】生物信号处理和管道设计。NeuXus是开源的，平台无关的，提供处理管道的高级实现，以便于BCI设计和部署。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
