Hironaga N, Ioannides A A
Laboratory for Human Brain Dynamics, RIKEN Brain Science Institute (BSI), Wako-shi, Saitama 351-0198, Japan.
Neuroimage. 2007 Feb 15;34(4):1519-34. doi: 10.1016/j.neuroimage.2006.10.030. Epub 2006 Dec 21.
A family of methods, collectively known as independent component analysis (ICA), has recently been added to the array of methods designed to decompose a multi-channel signal into components. ICA methods have been applied to raw magnetoencephalography (MEG) and electroencephalography (EEG) signals to remove artifacts, especially when sources such as power line or cardiac activity generate strong components that dominate the signal. More recently, successful ICA extraction of stimulus-evoked responses has been reported from single-trial raw MEG and EEG signals. The extraction of weak components has often been erratic, depending on which ICA method is employed and even on what parameters are used. In this work, we show that if the emphasis is placed on individual "independent components," as is usually the case with standard ICA applications, differences in the results obtained for different components are exaggerated. We propose instead the reconstruction of regional brain activations by combining tomographic estimates of individual independent components that have been selected by appropriate spatial and temporal criteria. Such localization of individual area neuronal activity (LIANA) allows reliable semi-automatic extraction of single-trial regional activations from raw MEG data. We demonstrate the new method with three different ICA algorithms applied to both computer-generated signals and real data. We show that LIANA provides almost identical results with each ICA method despite the fact that each method yields different individual components.
最近，一组统称为独立成分分析（ICA）的方法被添加到了旨在将多通道信号分解为各个成分的方法阵列中。ICA方法已应用于原始脑磁图（MEG）和脑电图（EEG）信号以去除伪迹，尤其是当诸如电源线或心脏活动等源产生主导信号的强成分时。最近，已有报道称从单次试验的原始MEG和EEG信号中成功提取出刺激诱发反应。弱成分的提取往往不稳定，这取决于所采用的ICA方法，甚至取决于所使用的参数。在这项工作中，我们表明，如果像标准ICA应用中通常那样将重点放在各个“独立成分”上，那么不同成分所获得结果的差异就会被夸大。相反，我们建议通过结合根据适当的空间和时间标准选择的各个独立成分的断层估计来重建区域脑激活。这种个体区域神经元活动定位（LIANA）允许从原始MEG数据中可靠地半自动提取单次试验的区域激活。我们用三种不同的ICA算法对计算机生成的信号和真实数据进行演示，展示了这种新方法。我们表明，尽管每种方法产生不同的个体成分，但LIANA使用每种ICA方法都能提供几乎相同的结果。
