ArticleThe European journal of neuroscience2026
Optimising Analysis Choices for Multivariate Decoding: Creating Pseudotrials Using Trial Averaging and Resampling.
Article in The European journal of neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Evaluating the effects of regularization and cross-validation parameters on the performance of SVM-based decoding of EEG data.Cortex; a journal devoted to the study of the nervous system and behavior · 2026Article
- Optimising Analysis Choices for Multivariate Decoding: Creating Pseudotrials Using Trial Averaging and Resampling.The European journal of neuroscience · 2026Article
- Evaluating the effects of regularization and cross-validation parameters on the performance of SVM-based decoding of EEG data.bioRxiv : the preprint server for biology · 2026Article
- Aligned representation of visual and tactile motion directions in hMT+/V5 and fronto-parietal regions.Nature communications · 2026Article
- Neural decoding of competitive decision-making in Rock-Paper-Scissors.Social cognitive and affective neuroscience · 2025Article
- Cross-modal congruency modulates evidence accumulation, not decision thresholds.Frontiers in neuroscience · 2025Article
- Visual imagery of familiar people and places in category selective cortex.Neuroscience of consciousness · 2025Article
- Distinct and complementary mechanisms of oscillatory and aperiodic alpha activity in visuospatial attention.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Aperiodic and oscillatory systems underpinning human domain-general cognition.Communications biology · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
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 robust condition-specific information in neuroimaging data, which may be missed by univariate methods. However, several analysis choices influence decoding results, and the combined effects of these choices have not been fully evaluated. In particular, an increasingly popular approach is to average data from several trials together before training an MVPA classifier, but the decision about how much averaging to do is arbitrary and the effect of varying this parameter has not been documented. Here, we systematically assessed the influence of trial averaging and resampling on decoding accuracy and subsequent statistical outcome on data simulated using two different toolboxes (CoSMoMVPA and SEREEGA). Although the optimal parameters varied with the classifier and cross-validation approach used, we found that modest trial averaging using up to 5%-10% of the total number of trials per condition improved decoding accuracy and associated t-statistics. In addition, a small amount of resampling could improve t-statistics and classification performance, but was not always necessary. We provide code to allow researchers to optimise these analysis choices for the parameters of their data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.