Pourtois Gilles, Delplanque Sylvain, Michel Christoph, Vuilleumier Patrik
Laboratory for Behavioral Neurology & Imaging of Cognition, Department of Neuroscience & Clinic of Neurology, University of Geneva, Geneva, Switzerland.
Brain Topogr. 2008 Jun;20(4):265-77. doi: 10.1007/s10548-008-0053-6. Epub 2008 Mar 13.
Recent technological advances with the scalp EEG methodology allow researchers to record electric fields generated in the human brain using a large number of electrodes or sensors (e.g. 64-256) distributed over the head surface (multi-channel recording). As a consequence, such high-density ERP mapping yields fairly dense ERP data sets that are often hard to analyze comprehensively or to relate straightforwardly to specific cognitive or emotional processes, because of the richness of the recorded signal in both the temporal (millisecond time-resolution) and spatial (multidimensional topographic information) domains. Principal component analyses (PCA) and topographic analyses (combined with distributed source localization algorithms) have been developed and successfully used to deal with this complexity, now offering powerful alternative strategies for data-driven analyses in complement to more traditional ERP analyses based on waveforms and peak measures. In this paper, we first briefly review the basic principles of these approaches, and then describe recent ERP studies that illustrate how they can inform about the precise spatio-temporal dynamic of emotion processing. These studies show that the perception of emotional visual stimuli may produce both quantitative and qualitative changes in the electric field configuration recorded at the scalp level, which are not apparent when using conventional ERP analyses. Additional information gained from these approaches include the identification of a sequence of successive processing stages that may not fully be reflected in ERP waveforms only, and the segregation of multiple or partly overlapping neural events that may be blended within a single ERP waveform. These findings highlight the added value of such alternative analyses when exploring the electrophysiological manifestations of complex and distributed mental functions, as for instance during emotion processing.
头皮脑电图方法的最新技术进展使研究人员能够使用分布在头部表面的大量电极或传感器（例如64 - 256个）记录人类大脑中产生的电场（多通道记录）。因此，这种高密度的ERP映射产生了相当密集的ERP数据集，由于记录信号在时间（毫秒时间分辨率）和空间（多维地形信息）领域都很丰富，这些数据集往往很难全面分析或直接与特定的认知或情感过程相关联。主成分分析（PCA）和地形分析（结合分布式源定位算法）已经得到发展并成功用于处理这种复杂性，现在为数据驱动的分析提供了强大的替代策略，以补充基于波形和峰值测量的更传统的ERP分析。在本文中，我们首先简要回顾这些方法的基本原理，然后描述最近的ERP研究，这些研究说明了它们如何能够揭示情绪处理精确的时空动态。这些研究表明，对情绪化视觉刺激的感知可能会在头皮水平记录的电场配置中产生定量和定性的变化，而在使用传统ERP分析时这些变化并不明显。从这些方法中获得的其他信息包括识别一系列连续的处理阶段，这些阶段可能不会完全反映在ERP波形中，以及分离可能在单个ERP波形中混合的多个或部分重叠的神经事件。这些发现突出了在探索复杂和分布式心理功能的电生理表现时，如在情绪处理过程中，这种替代分析的附加价值。
