2020
DOI: 10.1101/2020.12.03.410910
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Deep-Learning-Based Multivariate Pattern Analysis (dMVPA): A Tutorial and a Toolbox

Abstract: In recent years, multivariate pattern analysis (MVPA) has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and other neuroimaging methodologies. In a similar time frame, “deep learning” (a term for the use of artificial neural networks with convolutional, recurrent, or similarly sophisticated architectures) has produced a parallel revolution in the fi… Show more

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Cited by 2 publications
(2 citation statements)
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“…Traditionally, data analysis has primarily been limited to a univariate approach such as detecting differences in activity between experimental conditions. In contrast, MVPA is concerned with how multivariate neural patterns comprising spatial and temporal combinations might collectively correspond to a cognitive event or state of interest (Kuntzelman et al 2021). As such, MVPA is a powerful technique to demonstrate the availability of discriminatory or predictive information, without requiring many assumptions about the underlying spatial or temporal extent of that information (Hogendoorn 2015).…”
Section: Methodsmentioning
confidence: 99%
“…Traditionally, data analysis has primarily been limited to a univariate approach such as detecting differences in activity between experimental conditions. In contrast, MVPA is concerned with how multivariate neural patterns comprising spatial and temporal combinations might collectively correspond to a cognitive event or state of interest (Kuntzelman et al 2021). As such, MVPA is a powerful technique to demonstrate the availability of discriminatory or predictive information, without requiring many assumptions about the underlying spatial or temporal extent of that information (Hogendoorn 2015).…”
Section: Methodsmentioning
confidence: 99%
“…No Class., Segm., Synthesis DeepVOG [58] Oculography Img, Vid Demo Segm. DeLINEATE [47] General Img, sequences External Class. DNNBrain [59] Brain mapping…”
Section: Data Type Datasets Taskmentioning
confidence: 99%