Open AccessAn Empirically Driven Guide on Using Bayes Factors for M/EEG Decoding
Author(s) -
Lina Teichmann,
Denise Moerel,
Chris I. Baker,
Tijl Grootswagers
Publication year - 2022
Language(s) - English
DOI - 10.52294/82179f90-eeb9-4933-adbe-c2a454577289
Subject(s) - bayes' theorem , bayes factor , magnetoencephalography , frequentist inference , computer science , artificial intelligence , bayesian probability , naive bayes classifier , neuroimaging , machine learning , bayesian inference , psychology , electroencephalography , psychiatry , support vector machine
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.