论文标题
iCanclean算法：如何使用参考噪声记录去除伪影
The iCanClean Algorithm: How to Remove Artifacts using Reference Noise Recordings
论文作者
论文摘要
数据记录通常会因噪声而损坏，并且很难隔离清晰的感兴趣数据。例如，移动脑电图通常被运动伪像损坏，这限制了其在现实世界中的使用。在这里，我们描述了一种新颖的噪声算法，该算法使用规范相关分析来查找和删除与参考噪声记录的子空间最密切相关的损坏数据记录的子空间。该算法称为icanclean，在计算上是有效的，它可能对实时应用（例如大脑计算机接口）有用。在将来的工作中，我们将量化该算法的性能，并将其与替代清洁方法进行比较。
Data recordings are often corrupted by noise, and it can be difficult to isolate clean data of interest. For example, mobile electroencephalography is commonly corrupted by motion artifact, which limits its use in real-world settings. Here, we describe a novel noise-canceling algorithm that uses canonical correlation analysis to find and remove subspaces of corrupted data recordings that are most strongly correlated with subspaces of reference noise recordings. The algorithm, termed iCanClean, is computationally efficient, which may be useful for real-time applications, such as brain computer interfaces. In future work, we will quantify the algorithm's performance and compare it with alternative cleaning methods.
