An iterative subspace denoising algorithm for removing electroencephalogram ocular artifacts
An iterative subspace denoising algorithm for removing electroencephalogram ocular artifacts
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DOI：
10.1016/j.jneumeth.2014.01.024
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发表时间：
2014-03-30
影响因子：
3
通讯作者：
Gouy-Pailler, Cedric
中科院分区：
文献类型：
--
作者：
Sameni, Reza;Gouy-Pailler, Cedric
背景：脑电图（EEG）测量总是受到非大脑信号的污染，这会干扰脑电图的可解释性。在不同的伪影中，眼部伪影是最令人不安的。在之前的研究中，使用基于频率的方法取得了有限的改进。空间分解方法已被证明可以更有效地消除脑电图记录中的眼部伪影。然而，这些方法无法完全分离大脑和眼睛信号，并且通常会消除脑电图的重要特征。新方法：在之前的研究中，我们已经证明了基于广义特征值分解的紧缩算法对于分离所需和不需要的信号子空间的适用性。在这项工作中，我们将这一想法扩展到自动检测和去除多通道脑电图记录中的眼电图（EOG）伪影。有效可识别维数的概念也被用来估计眼子空间的主导维数，这使得算法能够精确、快速地收敛。结果：该方法应用于真实数据和合成数据。结果表明，该方法能够分离大脑和眼睛信号，同时对大脑信号的干扰最小。与现有方法的比较：将所提出的方法与两种广泛使用的基于独立成分分析（ICA）的去噪技术进行比较。结论：结果表明，该算法优于基于 ICA 的方法。此外，该方法计算效率高并且是实时实现的。由于其半自动化结构和较低的计算成本，在实时脑电监测系统和脑机接口实验中具有广泛的应用。 (C) 2014 Elsevier B.V. 保留所有权利。
Background: Electroencephalogram (EEG) measurements are always contaminated by non-cerebral signals, which disturb EEG interpretability. Among the different artifacts, ocular artifacts are the most disturbing ones. In previous studies, limited improvement has been obtained using frequency-based methods. Spatial decomposition methods have shown to be more effective for removing ocular artifacts from EEG recordings. Nevertheless, these methods are not able to completely separate cerebral and ocular signals and commonly eliminate important features of the EEG.New method: In a previous study we have shown the applicability of a deflation algorithm based on generalized eigenvalue decomposition for separating desired and undesired signal subspaces. In this work, we extend this idea for the automatic detection and removal of electrooculogram (EOG) artifacts from multichannel EEG recordings. The notion of effective number of identifiable dimensions, is also used to estimate the number of dominant dimensions of the ocular subspace, which enables the precise and fast convergence of the algorithm.Results: The method is applied on real and synthetic data. It is shown that the method enables the separation of cerebral and ocular signals with minimal interference with cerebral signals.Comparison with existing method(s): The proposed approach is compared with two widely used denoising techniques based on independent component analysis (ICA).Conclusions: It is shown that the algorithm outperformed ICA-based approaches. Moreover, the method is computationally efficient and is implemented in real-time. Due to its semi-automatic structure and low computational cost, it has broad applications in real-time EEG monitoring systems and brain-computer interface experiments. (C) 2014 Elsevier B.V. All rights reserved.