Automatic Identification and Removal of Ocular Artifacts in EEG—Improved Adaptive Predictor Filtering for Portable Applications
Automatic Identification and Removal of Ocular Artifacts in EEG—Improved Adaptive Predictor Filtering for Portable Applications
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DOI：
10.1109/tnb.2014.2316811
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发表时间：
2014-04
影响因子：
3.9
通讯作者：
Qinglin Zhao;Bin Hu;Yujun Shi;Yang Li;P. Moore;Ming-Hou Sun;Hong Peng
中科院分区：
文献类型：
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作者：
Qinglin Zhao;Bin Hu;Yujun Shi;Yang Li;P. Moore;Ming-Hou Sun;Hong Peng
脑电图（EEG）信号作为一种无创测量脑功能的方法已有很长的历史。在基于脑电图的应用中，一个重要的组成部分是从脑电图信号中去除眼伪影（OA）。本文提出了一种结合离散小波变换（DWT）和自适应预测滤波器（APF）的混合去噪方法。该方法的一个特别新颖的特点是使用基于自适应自回归模型的APF来预测眼部伪影区信号的波形。在我们的测试中，基于模拟数据，与现有的小波包变换（WPT）和独立分量分析（ICA）、离散小波变换（DWT）和自适应降噪（ANC）方法相比，所提出模型的降噪精度显著提高。实验结果表明，该方法具有较低的均方误差和较高的相关性。所提出的方法也已使用来自精神疾病干预在线预测工具（OPTIMI）项目校准试验的数据进行了评估。该评价结果表明，在脑电信号跟踪和分析计算速度方面，该方法在恢复真实脑电信号方面的性能有所提高。所提出的方法非常适合便携式环境中的应用，其中关于可接受的可穿戴传感器附件的约束通常规定了单通道设备。
Electroencephalogram (EEG) signals have a long history of use as a noninvasive approach to measure brain function. An essential component in EEG-based applications is the removal of Ocular Artifacts (OA) from the EEG signals. In this paper we propose a hybrid de-noising method combining Discrete Wavelet Transformation (DWT) and an Adaptive Predictor Filter (APF). A particularly novel feature of the proposed method is the use of the APF based on an adaptive autoregressive model for prediction of the waveform of signals in the ocular artifact zones. In our test, based on simulated data, the accuracy of noise removal in the proposed model was significantly increased when compared to existing methods including: Wavelet Packet Transform (WPT) and Independent Component Analysis (ICA), Discrete Wavelet Transform (DWT) and Adaptive Noise Cancellation (ANC). The results demonstrate that the proposed method achieved a lower mean square error and higher correlation between the original and corrected EEG. The proposed method has also been evaluated using data from calibration trials for the Online Predictive Tools for Intervention in Mental Illness (OPTIMI) project. The results of this evaluation indicate an improvement in performance in terms of the recovery of true EEG signals with EEG tracking and computational speed in the analysis. The proposed method is well suited to applications in portable environments where the constraints with respect to acceptable wearable sensor attachments usually dictate single channel devices.