  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.
