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Wavelet based Novel Technique for Signal Conditioning of Electro-Oculogram Signals

机译:基于小波的电力图信号信号调理的新技术

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In this paper, we present a novel and simple technique for signal conditioning of EOG signals which primarily involves denoising corrupted signals and post - processing for signal enhancement. Researches in the past have mainly focused on the EOG signals where the problem of removal of Ocular Artifacts from the Electroencephalogram was dealt. We present, from a new perspective, a scheme which essentially deals with enhancement of EOG Signals. The non-stationary and time-varying EOG signals are processed using methodologies anchored on multiresolution analyses and the Wavelet Transform theory. Coiflet wavelets are used for subsequent removal of noise from the (awgn) corrupted EOG signals using the concept of coefficient thresholding. SURE is used for threshold selection. Its performance, in terms of SNR, is compared with strategies suggested by Birge -Massart and Donoho and Johnstone. Haar based wavelets of higher orders are used for post-processing of EOG signals. A pronounced advantage of post - processing of signals is that it facilitates the estimation of time instants and durations of intentional eye gestures which mainly find application in the development of human - computer interface based devices.
机译:在本文中,我们提出了一种新颖简便的技术,用于Eog信号的信号调节,主要涉及去噪损坏的信号和信号增强后的后处理。过去的研究主要集中在Eog信号上,其中涉及从脑电图去除眼睛伪影的问题。我们从一个新的角度出示了一个基本上涉及Eog信号的增强的计划。使用锚定在多分辨率分析和小波变换理论上的方法处理非静止和时变的EOG信号。使用系数阈值的概念,Coiflet小波用于随后从(AWGN)损坏的EOG信号中的噪声移除。当然用于阈值选择。在SNR方面,其表现与Birge -Massart和Donoho和Johnstone建议的策略进行比较。基于HAAR的更高订单的小波用于EOG信号的后处理。后一个明显的优势 - 信号处理是便于时刻和蓄意眼睛姿势这在人类的发展主要是找到应用程序的持续时间的估计 - 基于计算机接口设备。

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