Optimising Analysis Choices for Multivariate Decoding: Creating Pseudotrials Using Trial Averaging and Resampling.
The 5 matches
- [1] § Methods › Simulation 2 ↔ analysis scripts/SEREEG_simulations_P300.m, lines 94–137 · score 0.88 · peak latency, peak width, location, midbrain, mixed, scalp
- [2] § Methods › Simulation 1 ↔ analysis scripts/permutation_testing.m, lines 45–103 · score 0.63 · cosmo montecarlo, sign flip permutation, alpha, score, simulations, resampled
- [3] § Methods › Simulation 1 ↔ analysis scripts/SEREEG_simulations_P300.m, lines 2–49 · score 0.59 · libSVM, cosmoMVPA, multivariate, LDA, Bayes, simulate
- [4] § Methods › Simulation 2 ↔ analysis scripts/permutation_testing.m, lines 45–103 · score 0.59 · cosmo montecarlo, sign flip permutation, alpha, score, simulation
- [5] § Results › Simulation 2: SEREEGA › Influence of Simulation Type ↔ analysis scripts/SEREEG_simulations_P300.m, lines 2–49 · score 0.54 · SEREEGA toolbox, P300, 13 %, ERP, amplitude, SVM
Paper
Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC
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The authors' code
MATLAB · 204 lines · 8.4 KB · no license · 3 matches
SEREEG_simulations_P300.m, no license · at the source
Overview
- MRC Cognition and Brain Sciences Unit University of Cambridge Cambridge UK
- School of Philosophy, Psychology and Language Sciences University of Edinburgh Edinburgh UK
- The MARCS Institute for Brain, Behaviour and Development Western Sydney University Sydney New South Wales Australia
- School of Computer, Data and Mathematical Sciences Western Sydney University Sydney New South Wales Australia
- Department of Psychology University of Cambridge Cambridge UK
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.
Reproduced under the paper's license (CC BY), from the paper cited above.
Repository
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OSF hjf75
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
- 27 September 2026: the link answers (HTTP 200)
4 files, to read at the source
This repository has no license: its authors keep all rights. Read it at the source.
- analysis scripts/
SEREEG_simulations_P300. — MATLAB, 204 lines, 3 matches, not shown herem - analysis scripts/
cosmomvpa_simulations.m — MATLAB, 180 lines, not shown here - analysis scripts/
permutation_testing.m — MATLAB, 186 lines, 2 matches, not shown here - analysis scripts/
viridis.m — MATLAB, 276 lines, not shown here
The paper's code and data availability statement is in the Data section.
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Data
No dataset and no data link were found in the paper.
Data Availability Statement
Analysis scripts can be found at https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 2, 28 September 2026
- Publisher: — → Wiley
- Authors: added Alexandra Woolgar (0000-0002-8453-7424); removed Alexandra Woolgar
Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 3 keywords, 6 MeSH terms, 3 funders, 20 references.
Cite
This paper
Scrivener, C. L., Grootswagers, T., & Woolgar, A. (2026). Optimising Analysis Choices for Multivariate Decoding: Creating Pseudotrials Using Trial Averaging and Resampling. The European journal of neuroscience, 64(2), e70601. https://
BibTeX
@article{scrivener2026op
author = {Scrivener, Catriona L. and Grootswagers, Tijl and Woolgar, Alexandra},
title = {{Optimising Analysis Choices for Multivariate Decoding: Creating Pseudotrials Using Trial Averaging and Resampling}},
journal = {The European journal of neuroscience},
year = {2026},
month = jul,
volume = {64},
number = {2},
pages = {e70601},
publisher = {Wiley},
issn = {0953-816X},
doi = {10.1111/
url = {https://
pmid = {42473262},
pmcid = {PMC13382189}
}
RIS
TY - JOUR
AU - Scrivener, Catriona L.
AU - Grootswagers, Tijl
AU - Woolgar, Alexandra
TI - Optimising Analysis Choices for Multivariate Decoding: Creating Pseudotrials Using Trial Averaging and Resampling
T2 - The European journal of neuroscience
J2 - Eur J Neurosci
PY - 2026
DA - 2026/
VL - 64
IS - 2
SP - e70601
SN - 0953-816X
PB - Wiley
DO - 10.1111/
UR - https://
LA - en
ER -
CSL-JSON
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