New concepts of multiple tests and their use for evaluating high-dimensional EEG data.

Recently, new concepts of type I error control in multiple comparisons have been proposed, in addition to FWE and FDR control. We introduce these criteria and investigate in simulations how the powers of corresponding test procedures for multiple endpoints depend on various quantities such as number and correlation of endpoints, percentage of false hypotheses, etc. We applied the different multiple tests to EEG coherence data.

Multivariate tests for the evaluation of high-dimensional EEG data.

In this paper several multivariate tests are presented, in particular permutation tests, which can be used in multiple endpoint problems as for example in comparisons of high-dimensional vectors of EEG data. We have investigated the power of these tests using artificial data in simulations and real EEG data. It is obvious that no one multivariate test is uniformly most powerful.