EOG artifact removal using a wavelet neural network
EOG artifact removal using a wavelet neural network
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DOI：
10.1016/j.neucom.2012.04.016
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发表时间：
2012-11-15
期刊：
影响因子：
6
通讯作者：
Li, Jiang
中科院分区：
文献类型：
--
作者：
Nguyen, Hoang-Anh T.;Musson, John;Li, Jiang
本文提出了一种小波神经网络（WNN）的脑电伪迹检测算法。该算法结合了神经网络的普适逼近特性和小波变换的时频特性，神经网络在具有已知地面真值的模拟数据集上进行训练。本文的贡献是双重的。首先，许多EEG伪影去除算法，包括基于回归的方法，需要参考EOG信号，这并不总是可用的。WNN算法试图从训练数据中学习EOG的特征，并且一旦训练好，该算法不需要EOG记录来去除伪影。其次，所提出的方法是计算效率，使其成为一个可靠的真实的时间算法。我们比较了所提出的算法的独立成分分析（伊卡）技术和自适应小波阈值的方法在模拟和真实的EEG数据集。实验结果表明，小波神经网络算法可以有效地去除EEG伪影，即使在非常嘈杂的数据集，而不减少有用的EEG信息。(C)2012爱思唯尔有限公司版权所有。
In this paper, we developed a wavelet neural network (WNN) algorithm for electroencephalogram (EEG) artifact. The algorithm combines the universal approximation characteristics of neural networks and the time/frequency property of wavelet transform, where the neural network was trained on a simulated dataset with known ground truths. The contribution of this paper is two-fold. First, many EEG artifact removal algorithms, including regression based methods, require reference EOG signals, which are not always available. The WNN algorithm tries to learn the characteristics of EOG from training data and once trained, the algorithm does not need EOG recordings for artifact removal. Second, the proposed method is computationally efficient, making it a reliable real time algorithm. We compared the proposed algorithm to the independent component analysis (ICA) technique and an adaptive wavelet thresholding method on both simulated and real EEG datasets. Experimental results show that the WNN algorithm can remove EEG artifacts effectively without diminishing useful EEG information even for very noisy datasets. (C) 2012 Elsevier B.V. All rights reserved.