Kummer Kilian, Dummel Sebastian, Bode Stefan, Stahl Jutta
Department of Psychology, University of Cologne, Germany.
Department of Psychology, University of Cologne, Germany.
J Neurosci Methods. 2020 Apr 1;335:108622. doi: 10.1016/j.jneumeth.2020.108622. Epub 2020 Feb 2.
Research using the event-related potential (ERP) method to investigate cognitive processes has usually focused on the analysis of either individual peaks or the area under the curve as components of interest. These approaches, however, do not analyse or describe the substantial variation in size and shape across the entire individual waveforms.
Here we show that the precision of ERP analyses can be improved by fitting gamma functions to components of interest. Gamma model analyses provide time-dependent and shape-related information about the component, such as the component's rise and decline. We demonstrated the advantages of the gamma model analysis in a simulation study and in a two-choice response task, as well as a force production task.
The gamma model parameters were sensitive to experimental variations, as well as variations in behavioural parameters.
Gamma model analyses provide researchers with additional reliable indicators about the shape of an ERP component's waveform, which previous analytical techniques could not.
This approach, therefore, provides a novel toolset to better understand the exact relationship between ERP components, behaviour and cognition.
使用事件相关电位（ERP）方法研究认知过程的研究通常专注于将单个峰值或曲线下面积作为感兴趣的成分进行分析。然而，这些方法并未分析或描述整个个体波形在大小和形状上的显著变化。
我们在此表明，通过将伽马函数拟合到感兴趣的成分上，可以提高ERP分析的精度。伽马模型分析提供了有关该成分的时间依赖性和形状相关信息，例如成分的上升和下降。我们在模拟研究、二选一反应任务以及力量产生任务中证明了伽马模型分析的优势。
伽马模型参数对实验变化以及行为参数的变化敏感。
伽马模型分析为研究人员提供了有关ERP成分波形形状的额外可靠指标，而这是以前的分析技术无法做到的。
因此，这种方法提供了一套新颖的工具集，以更好地理解ERP成分、行为和认知之间的确切关系。
