Department of Mathematics and Statistics, Department of Computer Science, HIIT, University of Helsinki, Finland.
Neuroimage. 2010 Jan 1;49(1):257-71. doi: 10.1016/j.neuroimage.2009.08.028. Epub 2009 Aug 20.
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.
自发脑电/脑磁图分析需要无监督学习方法。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/脑磁图中的技术伪影过于敏感。我们建议在脑电/脑磁图信号的短时傅里叶变换上应用 ICA，以便找到比时域 ICA 更“有趣”的源，并更有意义地对获得的分量进行分类。该方法特别适用于寻找节律活动的源。此外，我们建议使用复混合矩阵来模拟具有不同相位的空间扩展源。人工数据的模拟和静息状态脑磁图实验证明了该方法的实用性。
