Alexander Kevin E, Estepp Justin R, Elbasiouny Sherif M
Department of Biomedical, Industrial, and Human Factors Engineering, College of Engineering and Computer Science, Wright State University, Dayton, OH 45435, USA.
Oak Ridge Institute for Science and Education, Oak Ridge, TN 37831, USA.
Bioengineering (Basel). 2024 Jul 12;11(7):707. doi: 10.3390/bioengineering11070707.
(1) Background: The electroencephalogram (EEG) is frequently corrupted by ocular artifacts such as saccades and blinks. Methods for correcting these artifacts include independent component analysis (ICA) and recursive-least-squares (RLS) adaptive filtering (-AF). Here, we introduce a new method, AFFiNE, that applies Bayesian adaptive regression spline (BARS) fitting to the adaptive filter's reference noise input to address the known limitations of both ICA and RLS-AF, and then compare the performance of all three methods. (2) Methods: Artifact-corrected P300 morphologies, topographies, and measurements were compared between the three methods, and to known truth conditions, where possible, using real and simulated blink-corrupted event-related potential (ERP) datasets. (3) Results: In both simulated and real datasets, AFFiNE was successful at removing the blink artifact while preserving the underlying P300 signal in all situations where RLS-AF failed. Compared to ICA, AFFiNE resulted in either a practically or an observably comparable error. (4) Conclusions: AFFiNE is an ocular artifact correction technique that is implementable in online analyses; it can adapt to being non-stationarity and is independent of channel density and recording duration. AFFiNE can be utilized for the removal of blink artifacts in situations where ICA may not be practically or theoretically useful.
(1) 背景：脑电图（EEG）经常受到诸如扫视和眨眼等眼动伪迹的干扰。校正这些伪迹的方法包括独立成分分析（ICA）和递归最小二乘（RLS）自适应滤波（-AF）。在此，我们介绍一种新方法AFFiNE，它将贝叶斯自适应回归样条（BARS）拟合应用于自适应滤波器的参考噪声输入，以解决ICA和RLS-AF已知的局限性，然后比较这三种方法的性能。(2) 方法：使用真实和模拟的眨眼干扰事件相关电位（ERP）数据集，在三种方法之间比较伪迹校正后的P300形态、地形图和测量值，并在可能的情况下与已知真实情况进行比较。(3) 结果：在模拟和真实数据集中，在RLS-AF失败的所有情况下，AFFiNE都成功地去除了眨眼伪迹，同时保留了潜在的P300信号。与ICA相比，AFFiNE导致的误差在实际或可观察到的方面具有可比性。(4) 结论：AFFiNE是一种可用于在线分析的眼动伪迹校正技术；它能够适应非平稳性，并且与通道密度和记录持续时间无关。在ICA在实际或理论上可能无用的情况下，AFFiNE可用于去除眨眼伪迹。
