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Daniel Feuerriegel edited this page Nov 26, 2017 · 26 revisions

DDTBOX Documentation and Tutorials

Welcome to the DDTBOX wiki!

This documentation explains the core features of the Decision Decoding ToolBOX (DDTBOX) and how this toolbox can be used to perform multivariate pattern analyses (MVPA) on EEG and other types of data.

Tutorials on running MVPA and group-level analyses of decoding results are also included. The (advanced) sections contain information about the code structure for those who would like to perform nonstandard analyses or contribute to the toolbox. Other sections contain advice on running MVPA analyses on neuroimaging data, example datasets, and links to relavant literature.


1. Introduction to DDTBOX

  • The Decision Decoding ToolBOX (DDTBOX)
  • External dependencies

2. Getting started

  • What is DDTBOX, and what does it do?
  • Downloading, installing and configuring DDTBOX
  • Setting up LIBSVM and LIBLINEAR

3. Preparing your data for analyses in DDTBOX

  • Structure of epoched data used by DDTBOX
  • Epoched EEG data from EEGLAB and ERPLAB
  • Preprocessing EEG data
  • Independent component activations
  • Other types of data
  • Support vector regression (SVR) condition labels
  • Creating a channel locations file

4. Multivariate decoding methods

  • An overview of MVPA methods in DDTBOX
  • Decoding approaches for EEG data

5. Configuring DDTBOX for decoding analyses

  • The decoding configuration script
  • List of configurable settings for decoding
  • SVM backend flags (advanced)

6. Tutorial: Decoding epoched EEG data with DDTBOX

  • Tutorial overview
  • The tutorial dataset
  • The decoding approach
  • Step 1: Prepare the data for MVPA
  • Step 2: Create a decoding configuration script
  • Step 3: Run the classification analyses
  • Step 4: Plot the single subject results
  • Tutorial summary

7. Analysing decoding results

  • Overview of group-level analyses in DDTBOX
  • Decoding performance measures
  • Group-level testing of decoding performance

8. Feature weight analyses

  • What are SVM feature weights?
  • Group-level analyses of feature weights
  • Corrections for multiple comparisons
  • Custom analyses of feature weights (advanced)

9. Plotting and interpreting your decoding results

  • Single subject decoding results plots
  • Group decoding results plots
  • Using the custom plotting scripts
  • Changing default plotting settings

10. Configuring DDTBOX for group level analyses

  • List of configurable settings for group-level analyses

11. Tutorial: Analysing and plotting decoding results

  • Tutorial overview
  • Step 1: Organise the single subject decoding results and channel information files
  • Step 2: Create a group-level analysis configuration script
  • Step 3: Run group-level analyses and plot the results
  • Step 4: Access the outputs of group-level statistical analyses
  • Tutorial summary

MVPA resources and advice

  • Examples of MVPA of EEG data using DDTBOX
  • Advice on running multivariate pattern classification/regression analyses

Troubleshooting and support

  • Common issues
  • Frequently asked questions
  • How to ask questions and report bugs


  • Past workshops
  • Upcoming workshops

The DDTBOX mailing list

(Advanced) DDTBOX code structure

  • Decoding/MVPA functions
  • Group-level analysis functions

(Advanced) DDTBOX Backends

  • MATLAB Backend

(Advanced) Contributing to DDTBOX

  • New features and extensions
  • Bugfixes

(Extra) Preprocessing EEG data for MVPA

  • Resources for optimising classification performance

(Extra) Converting epoched data from EEGLAB and ERPLAB

  • Using dd_convert_eeg_data
  • Creating SVR condition labels
  • Example EEG processing scripts

(Extra) Correcting for multiple comparisons

  • Why do we correct for multiple comparisons?
  • Types of error rate control
  • Multiple comparisons correction methods implemented in DDTBOX
  • Comparing multiple comparisons correction methods

Features under development

  • List of proposed features

Release history


MVPA datasets

  • EEG datasets
  • Other types of datasets
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