Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis
Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis
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DOI：
10.1016/j.neuroimage.2009.08.028
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发表时间：
2010-01-01
期刊：
影响因子：
5.7
通讯作者：
Hari, Riitta
中科院分区：
文献类型：
--
作者：
Hyvarinen, Aapo;Ramkumar, Pavan;Hari, Riitta
自发脑电图/脑磁图的分析需要无监督学习方法。虽然独立成分分析 (ICA) 已成功应用于自发功能磁共振成像，但它似乎对脑电图/脑磁图的技术伪影过于敏感。我们建议将 ICA 应用于 EEG/MEG 信号的短时傅立叶变换，以便找到比时域 ICA 更“有趣”的源，并对获得的分量进行更有意义的排序。该方法对于寻找节律活动的来源特别有用。此外，我们建议使用复杂的混合矩阵来对空间扩展且在不同 EEG/MEG 通道中具有不同相位的源进行建模。人工数据模拟和静息态 MEG 实验证明了该方法的实用性。 (C) 2009 Elsevier Inc. 保留所有权利。
Analysis of spontaneous EEG/MEG needs unsupervised learning methods. While independent component analysis (ICA) has been successfully applied on spontaneous fMRI, it seems to be too sensitive to technical artifacts in EEG/MEG. We propose to apply ICA on short-time Fourier transforms of EEG/MEG signals, in order to find more "interesting" sources than with time-domain ICA, and to more meaningfully sort the obtained components. The method is especially useful for finding sources of rhythmic activity. Furthermore, we propose to use a complex mixing matrix to model Sources which are spatially extended and have different phases in different EEG/MEG channels. Simulations with artificial data and experiments on resting-state MEG demonstrate the utility of the method. (C) 2009 Elsevier Inc. All rights reserved.