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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Journal of cognitive neuroscience Ročník 29; číslo 4; s. 677 - 697
Hlavní autoři: Grootswagers, Tijl, Wardle, Susan G, Carlson, Thomas A
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 01.04.2017
Témata:
Brain - physiology
Evoked Potentials - physiology
Functional Neuroimaging - methods
Humans
Magnetoencephalography - methods
Multivariate Analysis
Signal Processing, Computer-Assisted
ISSN:1530-8898
On-line přístup:Zjistit podrobnosti o přístupu
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
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.
Bibliografie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-3
content type line 23
ObjectType-Review-1
ISSN:1530-8898
DOI:10.1162/jocn_a_01068