Optimising analysis choices for multivariate decoding: Creating pseudotrials using trial averaging and resampling

Catriona L. Scrivener*, Tijl Grootswagers, Alexandra Woolgar

*Corresponding author for this work

Research output: Working paperPreprint

Abstract / Description of output

Multivariate pattern analysis (MVPA) is a popular technique that can distinguish between condition-specific patterns of activation. Applied to neuroimaging data, MVPA decoding for inference uses above chance decoding to identify statistically reliable condition-specific information in neuroimaging data which may be missed by univariate methods. However, several analysis choices influence decoding success, and the combined effects of these choices have not been fully evaluated. We systematically assessed the influence of trial averaging and resampling on decoding accuracy and subsequent statistical outcome on simulated data. Although the optimal parameters varied with the classifier and cross-validation approach used, we found that modest trial averaging using roughly 5-10% of the total number of trials per condition improved accuracy and associated t-statistics. In addition, a resampling value of 2 could improve t-statistics and classification performance, but was not always necessary. We provide code to allow researchers to optimise analyses for the parameters of their data.
Original languageEnglish
Publication statusPublished - 6 Oct 2023

Keywords / Materials (for Non-textual outputs)

  • multivariate pattern analysis
  • decoding
  • pseudotrials

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