Robust adaptive techniques for minimization of EOG artefacts from EEG signals
Robust adaptive techniques for minimization of EOG artefacts from EEG signals
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DOI：
10.1016/j.sigpro.2005.10.018
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发表时间：
2006-09-01
影响因子：
4.4
通讯作者：
Ratnarajah, T.
中科院分区：
文献类型：
--
作者：
Puthusserypady, S.;Ratnarajah, T.
在本文中，我们提出了应用H-∞技术最小化的眼电（EOG）伪影从损坏的脑电（EEG）信号。提出了两种基于H ∞原理的自适应算法（时变和指数加权）。应用H-无穷技术的想法是出于这样一个事实，即它们对模型不确定性具有鲁棒性，并且缺乏关于噪声的统计信息[B。Hassibi，A.H. Sayed，T. Kailath，Krein空间中的线性估计-第1部分：理论和第11部分：应用，IEEE自动化。Control 41（1996）18-49]。对模拟的以及真实的记录的信号进行研究。所提出的技术的性能进行比较，然后与著名的最小均方（LMS）和递归最小二乘（RLS）算法。输出信噪比（SNR）的改善沿着与时间图被用作比较算法的性能的标准。结果发现，建议的H-无穷大为基础的算法的工作略好于RLS算法（特别是当输入信噪比很低），总是优于LMS算法，在最大限度地减少从损坏的EEG信号的EOG伪影。(c)2005 Elsevier B. V.保留所有权利。
In this paper, we propose the application of H-infinity techniques for minimization of electrooculogram (EOG) artefacts from corrupted electroencephalographic (EEG) signals. Two adaptive algorithms (time-varying and exponentially- weighted) based on the H-infinity principles are proposed. The idea of applying H-infinity techniques is motivated by the fact that they are robust to model uncertainties and lack of statistical information with respect to noise [B. Hassibi, A.H. Sayed, T. Kailath, Linear estimation in Krein spaces-Part 1: theory & Part 11: applications, IEEE Trans. Automat. Control 41 (1996) 18-49]. Studies are performed on simulated as well as real recorded signals. Performance of the proposed techniques are then compared with the well-known least-mean square (LMS) and recursive least-square (RLS) algorithms. Improvements in the output signal-to-noise ratio (SNR) along with the time plots are used as criteria for comparing the performance of the algorithms. It is found that the proposed H-infinity-based algorithms work slightly better than the RLS algorithm (especially when the input SNR is very low) and always outperform the LMS algorithm in minimizing the EOG artefacts from corrupted EEG signals. (c) 2005 Elsevier B.V. All rights reserved.