Multivariate pattern analysis (MVPA) or brain decoding methods have become
standard practice in analysing fMRI data. Although decoding methods have been
extensively applied in Brain Computing Interfaces (BCI), 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 generalisation, and
the interpretation of classifier weight maps. Finally, we outline important
caveats in the design and interpretation of time-series decoding experiments.Comment: 64 pages, 15 figure
