Winkler Irene, Debener Stefan, Müller Klaus-Robert, Tangermann Michael
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:4101-5. doi: 10.1109/EMBC.2015.7319296.
Standard artifact removal methods for electroencephalographic (EEG) signals are either based on Independent Component Analysis (ICA) or they regress out ocular activity measured at electrooculogram (EOG) channels. Successful ICA-based artifact reduction relies on suitable pre-processing. Here we systematically evaluate the effects of high-pass filtering at different frequencies. Offline analyses were based on event-related potential data from 21 participants performing a standard auditory oddball task and an automatic artifactual component classifier method (MARA). As a pre-processing step for ICA, high-pass filtering between 1-2 Hz consistently produced good results in terms of signal-to-noise ratio (SNR), single-trial classification accuracy and the percentage of `near-dipolar' ICA components. Relative to no artifact reduction, ICA-based artifact removal significantly improved SNR and classification accuracy. This was not the case for a regression-based approach to remove EOG artifacts.
用于脑电图（EEG）信号的标准伪迹去除方法要么基于独立成分分析（ICA），要么通过对在眼电图（EOG）通道测量的眼部活动进行回归来去除。基于ICA的成功伪迹减少依赖于合适的预处理。在这里，我们系统地评估了不同频率下高通滤波的效果。离线分析基于21名参与者执行标准听觉Oddball任务的事件相关电位数据以及一种自动伪迹成分分类器方法（MARA）。作为ICA的预处理步骤，在1-2Hz之间进行高通滤波在信噪比（SNR）、单次试验分类准确率和“近偶极”ICA成分百分比方面始终产生良好的结果。相对于未进行伪迹减少，基于ICA的伪迹去除显著提高了SNR和分类准确率。基于回归的去除EOG伪迹的方法则并非如此。
