Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data

Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analyzing fMRI data. Although decoding methods have been extensively applied in brain-computer interfaces, these methods have only recently been applied to time series neuroimaging data such as MEG and EE...

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Vydané v:Journal of cognitive neuroscience Ročník 29; číslo 4; s. 677 - 697
Hlavní autori: Grootswagers, Tijl, Wardle, Susan G, Carlson, Thomas A
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States 01.04.2017
Predmet:
Brain - physiology
Evoked Potentials - physiology
Functional Neuroimaging - methods
Humans
Magnetoencephalography - methods
Multivariate Analysis
Signal Processing, Computer-Assisted
ISSN:1530-8898
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Shrnutí:Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analyzing fMRI data. Although decoding methods have been extensively applied in brain-computer interfaces, these methods have only recently been applied to time series neuroimaging data such as MEG and EEG to address experimental questions in cognitive neuroscience. In a tutorial style review, we describe a broad set of options to inform future time series decoding studies from a cognitive neuroscience perspective. Using example MEG data, we illustrate the effects that different options in the decoding analysis pipeline can have on experimental results where the aim is to "decode" different perceptual stimuli or cognitive states over time from dynamic brain activation patterns. We show that decisions made at both preprocessing (e.g., dimensionality reduction, subsampling, trial averaging) and decoding (e.g., classifier selection, cross-validation design) stages of the analysis can significantly affect the results. In addition to standard decoding, we describe extensions to MVPA for time-varying neuroimaging data including representational similarity analysis, temporal generalization, and the interpretation of classifier weight maps. Finally, we outline important caveats in the design and interpretation of time series decoding experiments.
Bibliografia:ObjectType-Article-2
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ObjectType-Review-1
ISSN:1530-8898
DOI:10.1162/jocn_a_01068