# Decoding dynamic brain patterns from evoked responses: A tutorial on multivariate pattern analysis applied to time-series neuroimaging data

arXiv: https://arxiv.org/abs/1606.02840 · HTML: https://mlsanity.com/paper/1606.02840/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2016.

## Built on by (most cited first, 9 of 9)

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- [The Neural Correlates of Image Texture in the Human Vision Using Magnetoencephalography](https://mlsanity.com/paper/2111.09118/) (2021)
- [Decoding EEG Signals to Explore Next-Word Predictability in the Human Brain](https://mlsanity.com/paper/2607.18321/) (2026)
- [Best Practices in EEG Analysis: Preprocessing, Modeling, and Machine Learning](https://mlsanity.com/paper/2609.36609/) (2026)

## Abstract (arXiv)

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.