Sadasivan P K, Dutt D N
Department of Electrical Communication Engineering, Indian Institute of Science, Bangalore, India.
Comput Biomed Res. 1996 Feb;29(1):27-40. doi: 10.1006/cbmr.1996.0003.
One of the most important applications of adaptive systems is in noise cancellation using adaptive filters. In this paper, we propose adaptive noise cancellation schemes for the enhancement of EEG signals in the presence of EOG artifacts. The effect of two reference inputs is studied on simulated as well as recorded EEG signals and it is found that one reference input is enough to get sufficient minimization of EOG artifacts. This has been verified through correlation analysis also. We use signal to noise ratio and linear prediction spectra, along with time plots, for comparing the performance of the proposed schemes for minimizing EOG artifacts from contaminated EEG signals. Results show that the proposed schemes are very effective (especially the one which employs Newton's method) in minimizing the EOG artifacts from contaminated EEG signals.
自适应系统最重要的应用之一是使用自适应滤波器进行噪声消除。在本文中，我们提出了自适应噪声消除方案，用于在存在眼电（EOG）伪迹的情况下增强脑电图（EEG）信号。研究了两个参考输入对模拟以及记录的EEG信号的影响，发现一个参考输入就足以充分最小化EOG伪迹。这也通过相关性分析得到了验证。我们使用信噪比和线性预测谱以及时间图，来比较所提出的从受污染的EEG信号中最小化EOG伪迹方案的性能。结果表明，所提出的方案（特别是采用牛顿法的方案）在从受污染的EEG信号中最小化EOG伪迹方面非常有效。
