Max Planck Institute for Neurological Research, Cologne, Germany.
Neuroimage. 2011 Feb 1;54(3):2105-15. doi: 10.1016/j.neuroimage.2010.10.033. Epub 2010 Oct 18.
Following the development of increasingly precise measurement instruments and fine-grain analysis tools for electroencephalographic (EEG) data, analysis of single-trial event-related EEG has considerably widened the utility of this non-invasive method to investigate brain activity. Recently, independent component analysis (ICA) has become one of the most prominent techniques for increasing the feasibility of single-trial EEG. This blind source separation technique extracts statistically independent components (ICs) from the EEG raw signal. By restricting the signal analysis to those ICs representing the processes of interest, single-trial analysis becomes more flexible. Still, the selection-criteria for in- or exclusion of certain ICs are largely subjective and unstandardized, as is the actual selection process itself. We present a rationale for a bottom-up, data-driven IC selection approach, using clear-cut inferential statistics on both temporal and spatial information to identify components that significantly contribute to a certain event-related brain potential (ERP). With time-range being the only necessary input, this approach considerably reduces the pre-assumptions for IC selection and promotes greater objectivity of the selection process itself. To test the validity of the approach presented here, we present results from a simulation and re-analyze data from a previously published ERP experiment on error processing. We compare the ERP-based IC selections made by our approach to the selection made based on mere signal power. The comparison of ERP integrity, signal-to-noise ratio, and single-trial properties of the back-projected ICs outlines the validity of the approach presented here. In addition, functional validity of the extracted error-related EEG signal is tested by investigating whether it is predictive for subsequent behavioural adjustments.
随着脑电图（EEG）数据的测量仪器和细粒度分析工具的不断发展，单次事件相关 EEG 的分析大大拓宽了这种非侵入性方法研究大脑活动的应用范围。最近，独立成分分析（ICA）已成为提高单次 EEG 可行性的最突出技术之一。这种盲源分离技术从 EEG 原始信号中提取统计上独立的成分（ICs）。通过将信号分析限制在那些代表感兴趣过程的 IC 上，单次分析变得更加灵活。然而，某些 IC 的纳入或排除的选择标准在很大程度上是主观的和不标准化的，实际的选择过程也是如此。我们提出了一种自下而上、数据驱动的 IC 选择方法的基本原理，使用明确的推断统计对时间和空间信息进行分析，以识别对特定事件相关脑电位（ERP）有显著贡献的成分。由于只需要时间范围作为输入，这种方法大大减少了对 IC 选择的预先假设，并提高了选择过程本身的客观性。为了测试这里提出的方法的有效性，我们展示了一个模拟的结果，并重新分析了之前发表的关于错误处理的 ERP 实验的数据。我们将我们的方法基于 ERP 的 IC 选择与仅基于信号功率的选择进行比较。所提出的方法的 ERP 完整性、信噪比和反向投影 IC 的单次试验特性的比较概述了这里提出的方法的有效性。此外，通过研究它是否可以预测后续的行为调整，来测试提取的与错误相关的 EEG 信号的功能有效性。
