Demos

• Getting started exercise for RSA3.0
  • Introduction
  • Exercise 1: Data and RDM handling
  • Exercise 2: Fixed model inference
  • Exercise 3: Crossvalidation for flexible models
• Dataset objects in rsatoolbox
  • 1. Single-subject dataset example
  • Creating the dataset object
  • 2. Multi-subject dataset example
• Estimating dissimilarities
  • Calculating our first RDM
  • accessing RDM contents
  • To access the parts of the rdms object a few functions are available:
  • diagonal covariance from measurements = univariate noise normalization
  • shrinkage estimate from measurements = multivariate noise normalization
  • estimates based on residuals
  • Computing Mahalanobis distances
• Visualising RDMs with the 92 images dataset
  • Loading data and building RDMs instance with Icon pattern_descriptors
  • Plotting RDMs with text labels
  • Plotting with image labels
  • Optimising image label display and saving figure
  • Plot MDS
• Annotated RDM plots
  • Highlight
  • Contour
• Demo on unbalanced designs
  • Implementation explanation
• Temporal RSA
  • Load temporal data
  • The rsatoolbox.data.TemporalDataset class
  • create RDM movie
  • from here on
  • Model rdms
  • data - model similarity across time
• RSA on MEG data with MNE python
  • Steps:
  • 1a) loading, downsampling, epoching, equalizing event count
  • 1b) simulate multiple subjects
  • define candidate model RDMs
• EEG Demo
  • Introduction
  • Preprocess data
  • Importing the data to rsatoolbox
  • Calculate data RDMs
  • Annotations
  • Modelling
  • Further analysis
• Searchlight for RSA
  • Load data
• How to get an average RDM from a stack of partial RDMs