Miwakeichi Fumikazu, Martínez-Montes Eduardo, Valdés-Sosa Pedro A, Nishiyama Nobuaki, Mizuhara Hiroaki, Yamaguchi Yoko
Laboratory for Dynamics of Emergent Intelligence, RIKEN Brain Science Institute, Saitama 351-0198, Japan.
Neuroimage. 2004 Jul;22(3):1035-45. doi: 10.1016/j.neuroimage.2004.03.039.
Finding the means to efficiently summarize electroencephalographic data has been a long-standing problem in electrophysiology. A popular approach is identification of component modes on the basis of the time-varying spectrum of multichannel EEG recordings--in other words, a space/frequency/time atomic decomposition of the time-varying EEG spectrum. Previous work has been limited to only two of these dimensions. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) have been used to create space/time decompositions; suffering an inherent lack of uniqueness that is overcome only by imposing constraints of orthogonality or independence of atoms. Conventional frequency/time decompositions ignore the spatial aspects of the EEG. Framing of the data being as a three-way array indexed by channel, frequency, and time allows the application of a unique decomposition that is known as Parallel Factor Analysis (PARAFAC). Each atom is the tri-linear decomposition into a spatial, spectral, and temporal signature. We applied this decomposition to the EEG recordings of five subjects during the resting state and during mental arithmetic. Common to all subjects were two atoms with spectral signatures whose peaks were in the theta and alpha range. These signatures were modulated by physiological state, increasing during the resting stage for alpha and during mental arithmetic for theta. Furthermore, we describe a new method (Source Spectra Imaging or SSI) to estimate the location of electric current sources from the EEG spectrum. The topography of the theta atom is frontal and the maximum of the corresponding SSI solution is in the anterior frontal cortex. The topography of the alpha atom is occipital with maximum of the SSI solution in the visual cortex. We show that the proposed decomposition can be used to search for activity with a given spectral and topographic profile in new recordings, and that the method may be useful for artifact recognition and removal.
寻找有效总结脑电图数据的方法一直是电生理学中一个长期存在的问题。一种流行的方法是基于多通道脑电图记录的时变频谱识别成分模式——换句话说，是对时变脑电图频谱进行空间/频率/时间原子分解。以前的工作仅限于这些维度中的两个。主成分分析（PCA）和独立成分分析（ICA）已被用于创建空间/时间分解；但存在固有的非唯一性，只有通过施加原子的正交性或独立性约束才能克服。传统的频率/时间分解忽略了脑电图的空间方面。将数据构建为按通道、频率和时间索引的三维数组，允许应用一种称为平行因子分析（PARAFAC）的唯一分解。每个原子是分解为空间、频谱和时间特征的三线性分解。我们将这种分解应用于五名受试者在静息状态和心算期间的脑电图记录。所有受试者共有的是两个具有频谱特征的原子，其峰值在θ和α范围内。这些特征受生理状态调制，在静息阶段α增加，在心算阶段θ增加。此外，我们描述了一种新方法（源频谱成像或SSI），用于从脑电图频谱估计电流源的位置。θ原子的地形图是额叶的，相应SSI解的最大值在前额叶皮层。α原子的地形图是枕叶的，SSI解的最大值在视觉皮层。我们表明，所提出的分解可用于在新记录中搜索具有给定频谱和地形特征的活动，并且该方法可能有助于伪迹识别和去除。
