Onton Julie, Westerfield Marissa, Townsend Jeanne, Makeig Scott
Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093-0961, USA.
Neurosci Biobehav Rev. 2006;30(6):808-22. doi: 10.1016/j.neubiorev.2006.06.007. Epub 2006 Aug 14.
This review discusses the theory and practical application of independent component analysis (ICA) to multi-channel EEG data. We use examples from an audiovisual attention-shifting task performed by young and old subjects to illustrate the power of ICA to resolve subtle differences between evoked responses in the two age groups. Preliminary analysis of these data using ICA suggests a loss of task specificity in independent component (IC) processes in frontal and somatomotor cortex during post-response periods in older as compared to younger subjects, trends not detected during examination of scalp-channel event-related potential (ERP) averages. We discuss possible approaches to component clustering across subjects and new ways to visualize mean and trial-by-trial variations in the data, including ERP-image plots of dynamics within and across trials as well as plots of event-related spectral perturbations in component power, phase locking, and coherence. We believe that widespread application of these and related analysis methods should bring EEG once again to the forefront of brain imaging, merging its high time and frequency resolution with enhanced cm-scale spatial resolution of its cortical sources.
本综述讨论了独立成分分析（ICA）在多通道脑电图（EEG）数据中的理论及实际应用。我们使用了年轻和老年受试者执行视听注意力转移任务的示例，来说明ICA分辨两个年龄组诱发反应细微差异的能力。使用ICA对这些数据进行的初步分析表明，与年轻受试者相比，老年受试者在反应后阶段额叶和躯体运动皮层的独立成分（IC）过程中任务特异性丧失，而在头皮通道事件相关电位（ERP）平均值检查期间未检测到这种趋势。我们讨论了跨受试者成分聚类的可能方法以及可视化数据中均值和逐次试验变化的新方法，包括试验内和试验间动态的ERP图像图以及成分功率、锁相和相干性方面的事件相关频谱扰动图。我们相信，这些及相关分析方法的广泛应用应能使脑电图再次成为脑成像的前沿领域，将其高时间和频率分辨率与增强的皮层源厘米级空间分辨率相结合。
