Documentation and tutorials

Welcome to the MVPAlab wiki!. MVPAlab is a MATLAB-based and very flexible decoding toolbox for multidimensional electroencephalography and magnetoencephalography data. The MVPAlab Toolbox implements several machine learning algorithms to compute multivariate pattern analyses, cross-classification, temporal generalization matrices and feature and frequency contribution analyses. This toolbox has been designed to include an easy-to-use and very intuitive graphic user interface and data representation software, which makes MVPAlab a very convenient tool for those users with few or no previous coding experience. However, MVPAlab is not for beginners only, as it implements several high and low-level routines allowing more experienced users to design their own projects in a highly flexible manner.

Installation

• Compatibility and requirements
• Installation steps
• Run MVPAlab Toolbox

Getting started

• Prepare your data
• Graphic User Interface
  • Create new study
  • Configure the analysis
  • Compute a multivariate analysis
  • Plot the result
• Building custom scripts
  • Initialize MVPAlab toolbox
  • Run the configuration file
  • Import data
  • Compute a multivariate analysis
  • Plot the result

Analysis configuration

• Defining a configuration file
• Participants and data directories
• Trial average
• Balanced dataset
• Data normalization
• Data smoothing
• Analysis timing
• Channel selection
• Dimensionality reduction
• Classification model
• Cross-validation
• Performance metrics
• Parallel computation

Main decoding analyses

• Sample EEG dataset
• Multivariate Pattern Analysis
  • Overview
  • Demo scripts
• Multivariate Cross-Classification
  • Overview
  • Demo scripts
• Temporal generalization matrix
  • Overview
  • Demo scripts
• Feature contribution analysis
  • Overview
  • Configuration
  • Result representation
• Frequency contribution analysis
  • Overview
  • Configuration
  • Demo script

Statistics

• Cluster-based permutation test

Plot the results

• Result representation pipeline
  • Result representation pipeline
  • High-level plotting functions
  • Extra configuration parameters