论文标题
使用工件子空间重新策略对有限硬件进行移动脑电图校正
Mobile EEG artifact correction on limited hardware using artifact subspace recon- struction
论文作者
论文摘要
诸如脑电图（EEG）之类的生物学数据通常被称为伪影的不必要信号污染。因此，许多处理具有较低信噪比的生物数据的应用需要稳健的伪影校正。对于某些应用，例如脑部计算机间隙（BCI），伪像校正必须具有实时功能。伪影子空间重建（ASR）是脑电图减少伪影的统计方法。但是，在当前实施中，ASR不能轻松使用有限的硬件在移动数据记录中使用。在本报告中，我们通过描述有限的硬件（如单板计算机）的ASR实现，从而增加了便携式在线信号处理方法的增长领域。我们描述了用于研究平台的MATLAB代码库的体系结构，使用公开可用数据集的一组验证测试的过程。在有限的，便携式硬件上实施ASR，有助于在实验室环境外获得的脑电图信号的在线解释。
Biological data like electroencephalography (EEG) are typically contaminated by unwanted signals, called artifacts. Therefore, many applications dealing with biological data with low signal-to-noise ratio require robust artifact correction. For some applications like brain-computer-interfaces (BCI), the artifact correction needs to be real-time capable. Artifact subspace reconstruction (ASR) is a statistical method for artifact reduction in EEG. However, in its current implementation, ASR cannot be used in mobile data recordings using limited hardware easily. In this report, we add to the growing field of portable, online signal processing methods by describing an implementation of ASR for limited hardware like single-board computers. We describe the architecture, the process of translating and compiling a Matlab codebase for a research platform, and a set of validation tests using publicly available data sets. The implementation of ASR on limited, portable hardware facilitates the online interpretation of EEG signals acquired outside of the laboratory environment.
