Abstract
Time series and electroencephalographic data are often noisy sources of data. In addition, the samples are often small or medium so that confidence intervals for a given time point taken in isolation may be large. Decorrelation techniques were shown to be adequate and exact for repeated-measure designs where correlation is assumed constant across pairs of measurements. This assumption cannot be assumed in time series and electroencephalographic data where correlations are most-likely vanishing with temporal distance between pairs of points. Herein, we present a decorrelation technique based on an assumption of local correlation. This technique is illustrated with fMRI data from 14 participants and from EEG data from 24 participants.