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Local decorrelation for error bars in time series

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Doi: 10.20982/tqmp.20.2.p173

Cousineau, Denis , Proulx, Anthony , Potvin-Pilon, Annabelle , Fiset, Daniel
173-185
Keywords: Error bars , confidence intervals , time series , ERP , EEG , fMRI
Tools: R, Matlab, Mathematica
(data file)   (Appendix)

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.


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