Tutorials
The tutorials contain background on the different analysis methods and include code that you can copy-and-paste in MATLAB to walk through the different analysis options. Some of these tutorials are also used during the “Advanced EEG/MEG analysis” toolkit course that is presented at the Centre for Cognitive Neuroimaging of the Donders Institute for Brain, Cognition and Behaviour each year. Furthermore, we use or have used these tutorials during the various workshops.
The example datasets that are used in the documentation are listed in this overview, including links to all pages that use a specific dataset. These datasets are in general available from our download server.
We invite you to add your own tutorials to the website, considering the documentation guidelines. Whenever you explain somebody in person or over email how to do something with FieldTrip, please consider whether you could use the website for this, allowing others to learn from it as well.
See also the frequently asked questions and example scripts.
Introduction to FieldTrip and MATLAB
Reading and preprocessing data
- Preprocessing - Reading continuous EEG and MEG data
- Preprocessing - Segmenting and reading trial-based EEG and MEG data
- Introduction on dealing with artifacts
- Visual or manual artifact rejection
- Automatic artifact rejection
- Cleaning artifacts using ICA
Sensor-level analyses
- Event-related averaging and MEG planar gradient
- Preprocessing of EEG data and computing ERPs
- Preprocessing and event-related activity in combined MEG/EEG data
- Preprocessing of Optically Pumped Magnetometer (OPM) data
- Time-frequency analysis using Hanning window, multitapers and wavelets
- Time-frequency analysis of combined MEG/EEG data
- Sensor-level ERF, TFR and connectivity analyses
- Extracting the brain state and events from continuous sleep EEG
Source reconstruction
- Construct a headmodel for MEG source analysis
- Construct a BEM headmodel for EEG source analysis
- Construct a FEM headmodel for EEG source analysis
- Creating a source model for MEG or EEG source analysis
- Coregistration of optically pumped magnetometer (OPM) data
- Localizing electrodes using a 3D-scanner
- Localizing oscillatory sources in MEG data using a beamformer
- Beamforming oscillatory responses in combined MEG/EEG data
- Localizing visual gamma and cortico-muscular coherence
- Source reconstruction of event-related fields using minimum-norm estimation
- Dipole fitting of combined MEG/EEG data
- Computation of virtual MEG channels in source-space
- Virtual channel analysis of epilepsy MEG data
Analysis of intracranial data
- Analysis of human ECoG and sEEG recordings
- Analysis of monkey ECoG recordings
- Channel and source analysis of mouse EEG
- Preprocessing and analysis of spike-train data
- Preprocessing and analysis of spike and local field potential data
Analysis of TMS data
Analysis of fNIRS data
- Preprocessing and averaging of single-channel NIRS data
- Preprocessing and averaging of multi-channel NIRS data
Connectivity analysis
- Analysis of corticomuscular coherence
- Analysis of sensor- and source-level connectivity
- Extended analysis of sensor- and source-level connectivity
- Whole brain connectivity and network analysis
- Whole brain connectivity and network analysis (2) - EEG
Statistical analysis
- Parametric and non-parametric statistics on event-related fields
- Cluster-based permutation tests on event-related fields
- Cluster-based permutation tests on time-frequency data
- Statistical analysis and multiple comparison correction for combined MEG/EEG data
- Classification of event-related MEG data using MVPA-Light