Duffy F H, Jones K, Bartels P, Albert M, McAnulty G B, Als H
Harvard Medical School, Boston, Massachusetts.
Brain Topogr. 1990 Fall;3(1):3-12. doi: 10.1007/BF01128856.
Topographic mapping of brain electrical activity has become a commonly used method in the clinical as well as research laboratory. To enhance analytic power and accuracy, mapping applications often involve statistical paradigms for the detection of abnormality or difference. Because mapping studies involve many measurements and variables, the appearance of a large data dimensionality may be created. If abnormality is sought by statistical mapping procedures and if the many variables are uncorrelated, certain positive findings could be attributable to chance. To protect against this undesirable possibility we advocate the replication of initial findings on independent data sets. Statistical difference attributable to chance will not replicate, whereas real difference will reproduce. Clinical studies must, therefore, provide for repeat measurements and research studies must involve analysis of second populations. Furthermore, Principal Components Analysis can be employed to demonstrate that variables derived from mapping studies are highly intercorrelated and data dimensionality substantially less than the total number of variables initially created. This reduces the likelihood of capitalization on chance. The need to constrain alpha levels is not necessary when dimensionality is low and/or a second data set is available. When only one data set is available in research applications, techniques such as the Bonferroni correction, the "leave-one-out" method, and Descriptive Data Analysis (DDA) are available. These techniques are discussed, clinical and research examples are given, and differences between Exploratory (EDA) and Confirmatory Data Analysis (EDA) are reviewed.
脑电活动地形图已成为临床以及研究实验室中常用的方法。为了提高分析能力和准确性，地形图应用通常涉及用于检测异常或差异的统计范式。由于地形图研究涉及许多测量和变量，可能会出现大数据维度的情况。如果通过统计地形图程序寻找异常，并且如果许多变量不相关，某些阳性结果可能归因于偶然。为了防止这种不良可能性，我们主张在独立数据集上重复初始发现。归因于偶然的统计差异不会重复，而真正的差异会再现。因此，临床研究必须提供重复测量，研究研究必须涉及对第二个群体的分析。此外，可以采用主成分分析来证明从地形图研究中得出的变量高度相互关联，并且数据维度大大小于最初创建的变量总数。这降低了偶然获利的可能性。当维度较低和/或有第二个数据集时，没有必要限制α水平。在研究应用中只有一个数据集可用时，可以使用诸如邦费罗尼校正、“留一法”和描述性数据分析（DDA）等技术。讨论了这些技术，给出了临床和研究示例，并回顾了探索性数据分析（EDA）和验证性数据分析（CDA）之间的差异。
