Autoregressive model in the Lp norm space for EEG analysis
Autoregressive model in the Lp norm space for EEG analysis
复制标题
用于 EEG 分析的 Lp 范数空间中的自回归模型
DOI：
10.1016/j.jneumeth.2014.11.007
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发表时间：
2015-01-30
影响因子：
3
通讯作者：
Xu, Peng
中科院分区：
文献类型：
--
作者：
Li, Peiyang;Wang, Xurui;Xu, Peng
自回归（AR）模型广泛应用于脑电图（EEG）分析，例如波形拟合、频谱估计和系统识别。在实际应用中，脑电图不可避免地会受到意外的异常伪影的污染，这一点必须克服。然而，目前的AR模型大多基于12范数结构，由于L2范数的平方特性，夸大了异常值效应。本文在 Lp (p
The autoregressive (AR) model is widely used in electroencephalogram (EEG) analyses such as waveform fitting, spectrum estimation, and system identification. In real applications, EEGs are inevitably contaminated with unexpected outlier artifacts, and this must be overcome. However, most of the current AR models are based on the 12 norm structure, which exaggerates the outlier effect due to the square property of the L2 norm. In this paper, a novel AR object function is constructed in the Lp (p