Independent component analysis (ICA) is a blind source separation method to recover source signals of interest from their mixtures. Most existing ICA procedures assume independent sampling. Second-order-statistics-based source separation methods have been developed based on parametric time series models for the mixtures from the autocorrelated sources. However, the second-order-statistics-based methods cannot separate the sources accurately when the sources have temporal autocorrelations with mixed spectra. To address this issue, we propose a new ICA method by estimating spectral density functions and line spectra of the source signals using cubic splines and indicator functions, respectively. The mixed spectra and the mixing matrix are estimated by maximizing the Whittle likelihood function. We illustrate the performance of the proposed method through simulation experiments and an EEG data application. The numerical results indicate that our approach outperforms existing ICA methods, including SOBI algorithms. In addition, we investigate the asymptotic behavior of the proposed method.

翻译：独立成分分析(ICA)是一种盲源分离方法,用以从混合物中回收有兴趣的来源信号。大多数现有ICA程序都采用独立取样。基于二阶统计的源分离方法是根据与自动相关来源的混合物参数时间序列模型开发的。然而,基于二阶统计方法无法在来源与混合光谱有时间自动关系时准确区分来源。为解决这一问题,我们提议一种新的ICA方法,即分别利用立方根和指示功能估计源信号的光谱密度函数和线光谱。混合光谱和混合矩阵是通过尽量扩大Whittle可能性函数来估计的。我们通过模拟实验和EEG数据应用来说明拟议方法的性能。数字结果表明,我们的方法比现有的ICA方法,包括SOBI算法,要优于SOBI算法。此外,我们调查了拟议方法的无症状行为。