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首页> 外文期刊>Biomedical and Health Informatics, IEEE Journal of >Removal of Ocular Artifacts in EEG—An Improved Approach Combining DWT and ANC for Portable Applications
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Removal of Ocular Artifacts in EEG—An Improved Approach Combining DWT and ANC for Portable Applications

机译：去除脑电中的眼部伪影-一种结合DWT和ANC的便携式方法的改进方法

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摘要

A new model to remove ocular artifacts (OA) from electroencephalograms (EEGs) is presented. The model is based on discrete wavelet transformation (DWT) and adaptive noise cancellation (ANC). Using simulated and measured data, the accuracy of the model is compared with the accuracy of other existing methods based on stationary wavelet transforms and our previous work based on wavelet packet transform and independent component analysis. A particularly novel feature of the new model is the use of DWTs to construct an OA reference signal, using the three lowest frequency wavelet coefficients of the EEGs. The results show that the new model demonstrates an improved performance with respect to the recovery of true EEG signals and also has a better tracking performance. Because the new model requires only single channel sources, it is well suited for use in portable environments where constraints with respect to acceptable wearable sensor attachments usually dictate single channel devices. The model is also applied and evaluated against data recorded within the EUFP 7 Project—Online Predictive Tools for Intervention in Mental Illness (OPTIMI). The results show that the proposed model is effective in removing OAs and meets the requirements of portable systems used for patient monitoring as typified by the OPTIMI project.
机译：提出了一种从脑电图（EEG）去除眼部伪影（OA）的新模型。该模型基于离散小波变换（DWT）和自适应噪声消除（ANC）。使用模拟和测量的数据，将模型的准确性与其他基于固定小波变换的现有方法的准确性以及我们先前基于小波包变换和独立分量分析的工作进行了比较。新模型的一个特别新颖的功能是使用DWT通过EEG的三个最低频率小波系数来构建OA参考信号。结果表明，新模型在真实EEG信号的恢复方面显示出改进的性能，并且具有更好的跟踪性能。由于新模型仅需要单通道源，因此非常适合在便携式环境中使用，在这种环境中，可接受的可穿戴传感器附件的约束通常决定了单通道设备。该模型还根据EUFP 7项目-干预心理疾病的在线预测工具（OPTIMI）中记录的数据进行了应用和评估。结果表明，所提出的模型可有效消除OA，并满足OPTIMI项目所代表的用于患者监护的便携式系统的要求。

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