The Medical Image and Signal Processing (MEDISIP) Group, ELIS Department, Faculty of Engineering Sciences (Firw), Ghent University, The Institute for Broadband Technology (IBBT), Sint-Pietersnieuwstraat 41, 9000 Ghent, Belgium.
Comput Intell Neurosci. 2007;2007:75079. doi: 10.1155/2007/75079.
To cope with the severe masking of background cerebral activity in the electroencephalogram (EEG) by ocular movement artefacts, we present a method which combines lower-order, short-term and higher-order, long-term statistics. The joint smoothened subspace estimator (JSSE) calculates the joint information in both statistical models, subject to the constraint that the resulting estimated source should be sufficiently smooth in the time domain (i.e., has a large autocorrelation or self predictive power). It is shown that the JSSE is able to estimate a component from simulated data that is superior with respect to methodological artefact suppression to those of FastICA, SOBI, pSVD, or JADE/COM1 algorithms used for blind source separation (BSS). Interference and distortion suppression are of comparable order when compared with the above-mentioned methods. Results on patient data demonstrate that the method is able to suppress blinking and saccade artefacts in a fully automated way.
为应对眼动伪迹严重掩盖脑电图（EEG）中的背景脑活动，我们提出了一种结合低阶、短期和高阶、长期统计的方法。联合平滑子空间估计器（JSSE）在满足以下约束的情况下，在两个统计模型中计算联合信息：即，估计的源在时域中应该足够平滑（即，具有较大的自相关或自预测能力）。结果表明，与用于盲源分离（BSS）的 FastICA、SOBI、pSVD 或 JADE/COM1 算法相比，JSSE 能够从模拟数据中估计出一个组件，在抑制方法伪影方面具有优越性。与上述方法相比，干扰和失真抑制具有相当的水平。对患者数据的结果表明，该方法能够以全自动方式抑制眨眼和扫视伪迹。
