Objective: Analysis of the electroencephalogram (EEG) for epileptic spike and seizure detection or brain-computer interfaces can be severely hampered by the presence of artifacts. The aim of this study is to describe and evaluate a fast automatic algorithm for ongoing correction of artifacts in continuous EEG recordings, which can be applied offline and online. Methods: The automatic algorithm for ongoing correction of artifacts is based on fast blind source separation. It uses a sliding window technique with overlapping epochs and features in the spatial, temporal and frequency domain to detect and correct ocular, cardiac, muscle and powerline artifacts. Results: The approach was validated in an independent evaluation study on publicly available continuous EEG data with 2035 marked artifacts. Validation confirmed that 88% of the artifacts could be removed successfully (ocular: 81%, cardiac: 84%, muscle: 98%, powerline: 100%). It outperformed state-of-the-art algorithms both in terms of artifact reduction rates and computation time. Conclusions: Fast ongoing artifact correction successfully removed a good proportion of artifacts, while preserving most of the EEG signals. Significance: The presented algorithm may be useful for ongoing correction of artifacts, e.g., in online systems for epileptic spike and seizure detection or brain-computer interfaces.

翻译：目的: 脑电图(EEG)分析在癫痫棘波和发作检测或脑机接口中的应用，常因伪迹存在而严重受限。本研究旨在描述并评估一种快速自动算法，用于连续EEG记录中伪迹的持续校正，该算法可离线及在线应用。方法: 该基于快速盲源分离的自动持续伪迹校正算法，采用滑动窗口技术与重叠时段，利用空间、时域及频域特征检测并校正眼电、心电、肌电及工频伪迹。结果: 该方法在包含2035个标注伪迹的公开连续EEG数据独立评估研究中得以验证。验证结果表明，88%的伪迹可被成功移除(眼电:81%、心电:84%、肌电:98%、工频:100%)。该算法在伪迹降低率与计算时间两方面均优于现有最优算法。结论: 快速持续伪迹校正算法成功去除了大部分伪迹，同时保留了绝大多数EEG信号。意义: 所提算法可能适用于持续伪迹校正场景，例如在线癫痫棘波/发作检测系统或脑机接口应用。