Tampere Center for Child Health Research, University of Tampere School of Medicine, 33014, Tampere, Finland.
Behav Res Methods. 2014 Sep;46(3):745-57. doi: 10.3758/s13428-013-0404-4.
Recording of event-related potentials (ERPs) is one of the best-suited technologies for examining brain function in human infants. Yet the existing software packages are not optimized for the unique requirements of analyzing artifact-prone ERP data from infants. We developed a new graphical user interface that enables an efficient implementation of a two-stage approach to the analysis of infant ERPs. In the first stage, video records of infant behavior are synchronized with ERPs at the level of individual trials to reject epochs with noncompliant behavior and other artifacts. In the second stage, the interface calls MATLAB and EEGLAB (Delorme & Makeig, Journal of Neuroscience Methods 134(1):9-21, 2004) functions for further preprocessing of the ERP signal itself (i.e., filtering, artifact removal, interpolation, and rereferencing). Finally, methods are included for data visualization and analysis by using bootstrapped group averages. Analyses of simulated and real EEG data demonstrated that the proposed approach can be effectively used to establish task compliance, remove various types of artifacts, and perform representative visualizations and statistical comparisons of ERPs. The interface is available for download from http://www.uta.fi/med/icl/methods/eeg.html in a format that is widely applicable to ERP studies with special populations and open for further editing by users.
记录事件相关电位(ERPs)是研究人类婴儿大脑功能的最佳技术之一。然而,现有的软件包并没有针对分析婴儿易受干扰的 ERP 数据的独特要求进行优化。我们开发了一个新的图形用户界面,能够有效地实现分析婴儿 ERP 的两阶段方法。在第一阶段,婴儿行为的视频记录与个体试验级别的 ERP 同步,以拒绝具有不遵守行为和其他伪影的时期。在第二阶段,该界面调用 MATLAB 和 EEGLAB(Delorme 和 Makeig,《神经科学方法杂志》134(1):9-21,2004)函数,对 ERP 信号本身进行进一步的预处理(即滤波、去除伪影、插值和重新参考)。最后,包括使用引导组平均值进行数据可视化和分析的方法。对模拟和真实 EEG 数据的分析表明,所提出的方法可以有效地用于建立任务一致性、去除各种类型的伪影,并对 ERP 进行代表性的可视化和统计比较。该界面可从 http://www.uta.fi/med/icl/methods/eeg.html 以广泛适用于特殊人群 ERP 研究的格式下载,并可供用户进一步编辑。