目的:对癫痫发作和癫痫发作检测或脑-计算机接口的脑电图(EEG)分析可能会因存在伪影而受到严重阻碍。这项研究的目的是描述和评估一种快速自动算法,用于持续校正连续脑电图记录中的伪影,可以离线和在线应用。
方法:持续校正伪影的自动算法基于快速盲源分离。它使用滑动窗口技术,在空间中具有重叠的时期和特征,时域和频域来检测和校正眼睛,心脏,肌肉和电力线工件。
结果:该方法在对具有2035个标记伪影的公开连续EEG数据的独立评估研究中得到了验证。验证证实88%的伪影可以成功去除(眼部:81%,心脏:84%,肌肉:98%,电力线:100%)。在伪影减少率和计算时间方面,它都优于最先进的算法。
结论:快速持续的伪影校正成功地去除了相当比例的伪影,同时保留大部分脑电图信号。
结论:所提出的算法可能对正在进行的伪影校正有用,例如,用于癫痫发作和癫痫发作检测或脑机接口的在线系统。
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.
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.
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.
Fast ongoing artifact correction successfully removed a good proportion of artifacts, while preserving most of the EEG signals.
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.