An empirically driven guide on using Bayes factors for M/EEG decoding

Lina Teichmann; Denise Moerel; Chris Baker; Tijl Grootswagers

doi:10.52294/82179f90-eeb9-4933-adbe-c2a454577289

An empirically driven guide on using Bayes factors for M/EEG decoding

Lina Teichmann
, Denise Moerel
, Chris Baker
, Tijl Grootswagers

Research output: Contribution to journal › Article › peer-review

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Abstract

Bayes factors can be used to provide quantifiable evidence for contrasting hypotheses and have thus become increasingly popular in cognitive science. However, Bayes factors are rarely used to statistically assess the results of neuroimaging experiments. Here, we provide an empirically driven guide on implementing Bayes factors for time-series neural decoding results. Using real and simulated magnetoencephalography (MEG) data, we examine how parameters such as the shape of the prior and data size affect Bayes factors. Additionally, we discuss the benefits Bayes factors bring to analysing multivariate pattern analysis data and show how using Bayes factors can be used instead or in addition to traditional frequentist approaches.

Original language	English
Number of pages	10
Journal	Aperture Neuro
Volume	1
Issue number	8
DOIs	https://doi.org/10.52294/82179f90-eeb9-4933-adbe-c2a454577289
Publication status	Published - 2022

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This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 IGO License (https://creativecommons.org/licenses/by/4.0/legalcode), which permits the copy and redistribution of the material in any medium or format provided the original work and author are properly credited. In any reproduction of this article there should not be any suggestion that APERTURE NEURO or this article endorse any specific organization or products. The use of the APERTURE NEURO logo is not permitted. This notice should be preserved along with the article’s original URL. Open access logo and text by PLoS, under the Creative Commons Attribution-Share Alike 4.0 Unported license.

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10.52294/82179f90-eeb9-4933-adbe-c2a454577289

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An empirically driven guide on using Bayes factors for M/EEG decoding

Abstract

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