Wallstrom Garrick L, Kass Robert E, Miller Anita, Cohn Jeffrey F, Fox Nathan A
Center for Biomedical Informatics, University of Pittsburgh, Forbes Tower Suite 8084, 200 Lothrop Street, Pittsburgh, PA 15213, USA.
Int J Psychophysiol. 2004 Jul;53(2):105-19. doi: 10.1016/j.ijpsycho.2004.03.007.
A variety of procedures have been proposed to correct ocular artifacts in the electroencephalogram (EEG), including methods based on regression, principal components analysis (PCA) and independent component analysis (ICA). The current study compared these three methods, and it evaluated a modified regression approach using Bayesian adaptive regression splines to filter the electrooculogram (EOG) before computing correction factors. We applied each artifact correction procedure to real and simulated EEG data of varying epoch lengths and then quantified the impact of correction on spectral parameters of the EEG. We found that the adaptive filter improved regression-based artifact correction. An automated PCA method effectively reduced ocular artifacts and resulted in minimal spectral distortion, whereas ICA correction appeared to distort power between 5 and 20 Hz. In general, reducing the epoch length improved the accuracy of estimating spectral power in the alpha (7.5-12.5 Hz) and beta (12.5-19.5 Hz) bands, but it worsened the accuracy for power in the theta (3.5-7.5 Hz) band and distorted time domain features. Results supported the use of regression-based and PCA-based ocular artifact correction and suggested a need for further studies examining possible spectral distortion from ICA-based correction procedures.
人们已经提出了多种方法来校正脑电图（EEG）中的眼部伪迹，包括基于回归、主成分分析（PCA）和独立成分分析（ICA）的方法。本研究对这三种方法进行了比较，并评估了一种改进的回归方法，该方法使用贝叶斯自适应回归样条在计算校正因子之前过滤眼电图（EOG）。我们将每种伪迹校正程序应用于不同时长的真实和模拟EEG数据，然后量化校正对EEG频谱参数的影响。我们发现自适应滤波器改善了基于回归的伪迹校正。一种自动PCA方法有效地减少了眼部伪迹，并导致最小的频谱失真，而ICA校正似乎会使5至20Hz之间的功率发生失真。一般来说，缩短时长提高了估计α（7.5 - 12.5Hz）和β（12.5 - 19.5Hz）频段频谱功率的准确性，但降低了θ（3.5 - 7.5Hz）频段功率估计的准确性，并使时域特征发生失真。结果支持使用基于回归和基于PCA的眼部伪迹校正，并表明需要进一步研究基于ICA的校正程序可能产生的频谱失真。
