Local decorrelation for error bars in time series

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.

Keywords

• error bars
• confidence intervals
• time series
• erp
• eeg
• r, matlab, mathematica