pagelist /
Pagelist
- About the FieldTrip project
- Citations to the FieldTrip reference paper
- Copyrights
- Dataformats
- Development
- Accessing the FieldTrip source code through CVS
- Accessing the FieldTrip source code through Git
- Accessing the FieldTrip source code through SVN
- Contribute
- Data structures
- Deprecated functions and options
- Distributed computing
- Forward and inverse computation of EEG/MEG data
- Guidelines
- Code guidelines
- Communication guidelines
- Documentation guidelines
- Template guidelines
- Website syntax and formatting
- Integration between tools
- Meetings
- FieldTrip meeting - 20120822
- FieldTrip meeting - 20120905
- FieldTrip meeting - 20120912
- FieldTrip meeting - 20120919
- FieldTrip meeting - 20120926
- FieldTrip meeting - 20121024
- FieldTrip meeting - 20121114
- FieldTrip meeting - 20121121
- FieldTrip meeting - 20131030
- FieldTrip meeting - 20140319
- FieldTrip meeting - 20140326
- FieldTrip meeting - 20140402
- FieldTrip meeting - 20140409
- FieldTrip meeting - 20140416
- FieldTrip meeting - 20140423
- FieldTrip meeting - 20140430
- FieldTrip meeting - 20140514
- FieldTrip meeting - 20140521
- FieldTrip meeting - 20140528
- FieldTrip meeting - 20140604
- FieldTrip meeting - 20140611
- FieldTrip meeting - 20140618
- FieldTrip meeting - 20140625
- FieldTrip meeting - 20140702
- FieldTrip meeting - 20140709
- FieldTrip meeting - 20140716
- FieldTrip meeting - 20140723
- FieldTrip meeting - 20140730
- FieldTrip meeting - 20140806
- FieldTrip meeting - 20140813
- FieldTrip meeting - 20140903
- FieldTrip meeting - 20140917
- FieldTrip meeting - 20140923
- FieldTrip meeting - 20141008
- FieldTrip meeting - 20141015
- FieldTrip meeting - 20141029
- FieldTrip meeting - 20141029
- FieldTrip meeting - 20141105
- FieldTrip meeting - 20141126
- FieldTrip meeting - 20141203
- FieldTrip meeting - 20141215
- FieldTrip meeting - 20141222
- FieldTrip meeting - 20150112
- FieldTrip meeting - 20150126
- FieldTrip meeting - 20150202
- FieldTrip meeting - 20150209
- FieldTrip meeting - 20150216
- FieldTrip meeting - 20150223
- FieldTrip meeting - 20150302
- FieldTrip meeting - 20150309
- FieldTrip meeting - 20150316
- FieldTrip meeting - 20150330
- FieldTrip meeting - 20150413
- FieldTrip meeting - 20150504
- FieldTrip meeting - 20150518
- FieldTrip meeting - 20150601
- FieldTrip meeting - 20150608
- FieldTrip meeting - 20150615
- FieldTrip meeting - 20150622
- FieldTrip meeting - 20150629
- FieldTrip meeting - 20150706
- FieldTrip meeting - 20150713
- FieldTrip meeting - 20150720
- FieldTrip meeting - 20150831
- FieldTrip meeting - 20150907
- FieldTrip meeting - 20150914
- FieldTrip meeting - 20150921
- FieldTrip meeting - 20150928
- FieldTrip meeting - 20151005
- FieldTrip meeting - 20151019
- FieldTrip meeting - 20151026
- FieldTrip meeting - 20151102
- FieldTrip meeting - 20151109
- FieldTrip meeting - 20151116
- FieldTrip meeting - 20151123
- FieldTrip meeting - 20151130
- FieldTrip meeting - 20151207
- FieldTrip meeting - 20151214
- FieldTrip meeting - 20160104
- Modules
- Connectivity estimates for EEG/MEG time series data
- Distributed computing using a Linux compute cluster
- Forward computation of EEG/MEG source models
- Inverse source parameter estimates from EEG/MEG data
- Plotting of channel-level, source-level and other geometrical data related to EEG/MEG
- Preprocessing of EEG/MEG time series data
- Reading and writing of EEG/MEG time series data
- Spectral estimation of of EEG/MEG time series data
- Project overview
- A tutorial on forward modeling
- Add stripped spm2 and other toolboxes as external dependencies
- Add support for reading data from any file format supported by neuroshare
- Add the spike functions from Martin
- An alternative algorithm for constructing triangulated EEG-BEM head models
- CSP for classification
- Check the consistency between the documentation and the implementations
- Check the correctness of the implementation of the algorithms
- Clean up inside_vol and similar functions
- Clean up the buffer implementation
- Clean up the code of sourceanalysis, sourcedescriptives, freqdescriptives using checkdata
- Clean up the documentation on head modeling, anatomical processing, etc.
- Cleanup the functions in the private folders
- Code coverage
- Consistent flank detection for triggers
- Create a forward solver for charges in an infinite halfspace
- Create a headmodel for source reconstruction of MEG data
- Create a tutorial on the processing of animal data
- Create a volume conduction model of the head for source reconstruction of EEG data
- Dealing with TMS-EEG datasets
- Dealing with the geometry of the forward model
- Designing a custom 3D printed OPM helmet
- Developing the documentation of the source reconstruction methods
- Document grad.tra, modifications to it, and effects on inverse
- Ensemble methods
- Ensure consistency of the documentation
- Ensure consistent trial definition
- Ensure consistent units throughout fieldtrip
- Ensure that all website pages exist
- Ensure that the compat directories are NOT called by FieldTrip itself
- Explain how to create cfg.design for ft_xxxstatistics
- FieldTrip from a software development perspective
- FieldTrip/SIMBIO integration materials
- FieldTrip/SIMBIO integration plan
- FieldTrip/SIMBIO integration scenarios
- Handling of continuous data
- How to create a volumetric current density
- How to deal with the forward model units?
- Implement a common distributed computing backend
- Implement a consistent way to spatially transform a grid or source model
- Implement a function which computes a variety of bivariate coupling measures from the input data
- Implement a function which computes an mvar-model based on the input data
- Implement a graphical user interface as a "wizard" for certain analysis protocols
- Implement function that checks consistency of cfgs
- Implement online data processing and classification for BCI
- Implement support for CTF synthetic gradiometers
- Implement support for a separation of data into a signal and noise subspace
- Implement trial selection option
- Implementation of realistic electrode properties in forward volume conduction models
- Import and export data to and from MNE-Python
- Improve artifact handling
- Improve integration with other toolboxes
- Improve parallel computing under the hood
- Improve regression testing
- Infrastructure for testing
- Integration with NUTMEG
- Integration with SPM12
- Integration with SPM8
- Modularise ft_connectivityanalysis
- Move internal fcdc documentation onto the website
- NIRS development
- Neighbour templates
- Prefix all public functions with ft
- Provide an interface to the FNS software for FDM modelling
- Provide an interface to the OpenMEEG software for BEM modelling
- Redesign and implement a common statistical backend for various data types
- Redesign the interface to the read_fcdc_xxx functions
- Refurbishing the FORWARD module
- Reimplement the avg/cov/trial handling
- Replicate functionality of MNE software
- Restructure and rework all visualization functions
- Restructure the directory layout
- Switch from SPM2 to SPM8
- Switch to using the preproc functions and phase out the old preprocessing code
- Testing code quality
- Testing minimum-norm estimate in FieldTrip and in MNE Suite
- The rat beamformer
- Visualization concept for 4D bivariate data
- What is the best way to homogenize data using z-scores?
- Write a tutorial on how to work with the MEGSIM data
- source reconstruction using two dipoles
- Real-time data processing
- Closing the loop in a real-time BCI application
- FieldTrip buffer C implementation
- FieldTrip buffer C++ implementation
- FieldTrip buffer Java interface
- FieldTrip buffer MATLAB interface
- FieldTrip buffer Python interface
- FieldTrip buffer reference implementation
- Low-level FieldTrip buffer TCP network protocol
- Overview of the realtime buffer
- Pipeline architecture
- Realtime visualization of data from the FieldTrip buffer
- Scratchpad for the realtime buffer interface
- Specific software implementations for realtime EEG/MEG/fMRI/NIRS
- Streaming realtime EEG data to and from Arduino
- Streaming realtime data from ANT NeuroSDK
- Streaming realtime data from Artinis Medical Systems
- Streaming realtime data from BioSemi ActiveTwo EEG amplifier
- Streaming realtime data from BrainVision Recorder Remote Data Access (RDA)
- Streaming realtime data from CTF
- Streaming realtime data from Emotiv neuroheadset
- Streaming realtime data from Jinga-Hi
- Streaming realtime data from Micromed
- Streaming realtime data from Modular EEG (aka OpenEEG)
- Streaming realtime data from Neuralynx
- Streaming realtime data from Neuromag/Elekta/Megin
- Streaming realtime data from Neurosky ThinkCap
- Streaming realtime data from OpenBCI
- Streaming realtime data from TMSI EEG amplifiers
- Streaming realtime data from TOBI (Tools for Brain-Computer Interaction)
- Streaming realtime data from Unicorn Hybrid Black
- Streaming realtime data from and to BCI2000
- Streaming realtime data from and to BrainStream
- Streaming realtime data from soundcard using PortAudio
- Suggested changes to the network protocol
- Suggested changes to the reference implementation
- Suggested improvements for compatibility across versions
- Suggested improvements for handling header and chunks
- Testing with sine waves and pre-recorded EEG data
- Translating characters received on a serial port to FieldTrip events
- Release and quality control
- Reporting issues
- Software architecture
- Testing
- Documentation
- Download the FieldTrip toolbox
- Email discussion list
- Examples
- Examples for plotting and visualization
- Creating a layout for plotting NIRS optodes and channels
- Making a synchronous movie of EEG or NIRS combined with video recordings
- Plotting the result of source reconstruction on a cortical mesh
- Examples for reading and preprocessing data
- Combine MEG with Eyelink eyetracker data
- Detect the muscle activity in an EMG channel and use that as trial definition
- Determine the filter characteristics
- Fixing a missing channel
- Getting started with reading raw EEG or MEG data
- Independent component analysis (ICA) to remove ECG artifacts
- Independent component analysis (ICA) to remove EOG artifacts
- Making your own trialfun for conditional trial definition
- Rereference EEG and iEEG data
- Use denoising source separation (DSS) to remove ECG artifacts
- Examples for real-time analysis
- Example real-time average
- Example real-time classification
- Example real-time power estimate
- Example real-time selective average
- Example real-time signal viewer
- Measuring the timing delay and jitter for a real-time application
- Realtime neurofeedback application based on Hilbert phase estimation
- Examples for sensor-level analysis
- Analyzing NIRS data recorded during listening to and repeating speech
- Analyzing NIRS data recorded during unilateral finger- and foot-tapping
- How to incorporate head movements in MEG analysis
- Interpolating data from the CTF151 to the CTF275 sensor array using megrealign
- The correct pipeline order for combining planar MEG channels
- Examples for source reconstruction
- Align EEG electrode positions to BEM headmodel
- Check the quality of the anatomical coregistration
- Combined EEG and MEG source reconstruction
- Common filters in beamforming
- Compute EEG leadfields using a BEM headmodel
- Compute EEG leadfields using a FEM headmodel
- Compute EEG leadfields using a concentric spheres headmodel
- Compute forward simulated data and apply a beamformer scan
- Compute forward simulated data and apply a dipole fit
- Compute forward simulated data using ft_dipolesimulation
- Compute forward simulated data with the low-level ft_compute_leadfield
- Create MNI-aligned grids in individual head coordinates
- Create a template source model aligned to MNI space
- Create atlas-based MNI-aligned grids in individual head coordinates
- Fit a dipole to the tactile ERF after mechanical stimulation
- Fitting a template MRI to the MEG Polhemus head shape
- How to create a head model if you do not have an individual MRI
- Localizing the sources underlying the difference in event-related fields
- Make MEG leadfields using different headmodels
- Read Neuromag .fif mri and create a MNI-aligned single-shell head model
- Symmetric dipole pairs for beamforming
- Testing BEM created EEG lead fields
- Use an MNI-aligned grid with a FEM headmodel in individual head coordinates
- Use your own forward leadfield model in an inverse beamformer computation
- Examples for spectral analysis
- Analysis of high-gamma band signals in human ECoG
- Analyze steady-state visual evoked potentials (SSVEPs)
- Conditional Granger causality in the frequency domain
- Cross-frequency analysis
- Effect of SNR on Coherence
- Effects of tapering for power estimates
- Fitting oscillations and one-over-F (FOOOF)
- Fourier analysis of neuronal oscillations and synchronization
- Interpolate the time axis of single-trial TFRs
- Irregular resampling auto-spectral analysis (IRASA)
- Simulate an oscillatory signal with phase resetting
- Examples for statistical analysis
- Apply non-parametric statistics with clustering on TFRs of power that were computed with BESA
- Computing and reporting the effect size
- Defining electrodes as neighbours for cluster-level statistics
- Source statistics
- Stratify the distribution of one variable that differs in two conditions
- Use simulated ERPs to explore cluster statistics
- Using general linear modeling on time series data
- Using general linear modeling over trials
- Using general linear modeling to analyze NIRS timeseries data
- Using simulations to estimate the sample size for cluster-based permutation test
- Using threshold-free cluster enhancement for cluster statistics
- Various other examples
- BIDS - the brain imaging data structure
- Combining simultaneous recordings in BIDS
- Converting an example EEG dataset for sharing in BIDS
- Converting an example EMG dataset for sharing in BIDS
- Converting an example MEG dataset for sharing in BIDS
- Converting an example NIRS dataset for sharing in BIDS
- Converting an example audio dataset for sharing in BIDS
- Converting an example behavioral dataset for sharing in BIDS
- Converting an example eyetracker dataset for sharing in BIDS
- Converting an example motion tracking dataset for sharing in BIDS
- Converting an example video dataset for sharing in BIDS
- Converting the combined MEG/fMRI MOUS dataset for sharing in BIDS
- Correlation analysis of fMRI data
- Designing 3D printed helmets for different OPM sensor types
- Example analysis pipeline for BioSemi data
- Find the orientation of planar gradiometers
- How to import data from MNE-Python and FreeSurfer
- How to use ft_checkconfig
- Making your analysis pipeline reproducible using reproducescript
- Perform modified multiscale entropy (mMSE) analysis on EEG/MEG/LFP data
- Using reproducescript for a group analysis
- Using reproducescript on a full study
- External links
- FieldTrip Walkthrough
- FieldTrip workshops and courses
- Advanced MEG/EEG toolkit at the Donders
- Advanced MEG/EEG toolkit at the Donders
- Advanced MEG/EEG toolkit at the Donders
- Code of Conduct
- Lessons learned
- Preparation for the online toolkit
- Test your MATLAB and FieldTrip installation in advance
- Advanced MEG/EEG toolkit at the Donders
- Advanced MEG/EEG toolkit at the Donders
- Advanced MEG/EEG toolkit at the Donders
- Advanced MEG/EEG toolkit at the Donders
- Advanced MEG/EEG toolkit at the Donders
- Advanced analysis and source modeling of EEG and MEG data
- Advanced analysis and source modeling of EEG and MEG data
- Advanced analysis and source modeling of EEG and MEG data
- African Brain Data Network EEG workshop in Nigeria
- Event-related potentials
- Experimental design and presenting stimuli
- Frequency analysis of resting state EEG data
- Preprocessing of EEG data
- Biomag 2016 satellite meeting - Seoul, Korea
- Chengdu, China
- ChildBrain pre-conference workshop in Leuven, Belgium
- CuttingEEG X workshop at the Donders
- ECoG/sEEG FieldTrip bootcamp at UC Davis
- FieldTrip Workshop in Exeter, UK
- FieldTrip Workshop in Guangzhou, China
- FieldTrip Workshop in Göttingen in 2019
- FieldTrip Workshop in London, UK
- FieldTrip Workshop in Mannheim
- FieldTrip Workshop in Marseille in January 2016
- FieldTrip Workshop in Marseille in November 2016
- FieldTrip Workshop in Tübingen, Germany
- FieldTrip course at the NatMEG in Stockholm
- FieldTrip tutorial at CuttingEEG 2021 in Aix-en-Provence
- Convert the EEG language dataset for sharing in BIDS
- Time-frequency analysis on short and long timescales
- FieldTrip tutorial at WIRED 2024 in Paris
- FieldTrip tutorial at the Donders Cognition Brain and Technology school
- FieldTrip workshop Donostia
- FieldTrip workshop at ESI/MPI Frankfurt, Germany
- FieldTrip workshop at Universidad Católica del Maule, Chile
- FieldTrip workshop at Washington University, St Louis
- FieldTrip workshop in Aarhus
- FieldTrip workshop in Barcelona
- FieldTrip workshop in Bern, Switzerland
- Preprocessing of EEG data
- Solving the EEG forward problem using BEM and FEM
- Solving the EEG inverse problem
- FieldTrip workshop in Chieti
- MEG virtual channels and seed-based connectivity
- MEG whole-brain connectivity
- Simulating and estimating, what about model (mis)match?
- FieldTrip workshop in Coimbra, Portugal
- FieldTrip workshop in Göttingen in 2014
- FieldTrip workshop in Hamburg in 2012
- FieldTrip workshop in Hamburg in 2013
- FieldTrip workshop in Hamburg in 2023
- FieldTrip workshop in Kiel, Germany
- FieldTrip workshop in Krakow, Poland
- FieldTrip workshop in Madrid
- Cleaning and processing resting-state EEG
- Cluster-based permutation tests on resting-state EEG power spectra
- Convert the EEG sedation dataset for sharing in BIDS
- Details on the resting-state EEG dataset recorded with different sedation levels
- Getting started with EEG data, quality checks and ERPs
- Time-frequency and spectral analysis
- FieldTrip workshop in Oslo
- Beamforming oscillatory responses in MEG data
- Forward modeling for EEG source reconstruction
- Preprocessing and event-related potentials in EEG data
- Statistical analysis and multiple comparison correction for EEG data
- Time-frequency analysis of EEG data
- FieldTrip workshop in Parma
- FieldTrip workshop in Salzburg, Austria
- FieldTrip workshop in Salzburg, Austria
- FieldTrip workshop in Singapore
- Preprocessing of EEG and MEG data
- Solving the EEG and MEG forward problem using the finite element method
- Solving the EEG inverse problem
- FieldTrip workshop in Sofia, Bulgaria
- FieldTrip workshop in Stockholm
- FieldTrip workshop in Trento, Italy
- FieldTrip workshop in Zürich
- Interactive Virtual Workshop organized by the Fetal, Infant, & Toddler Neuroimaging Group
- Joint FieldTrip/MNE course at the NatMEG in Stockholm
- M/EEG analysis workshop in Jyväskylä, Finland
- MEG-UK 2015 meeting
- FieldTrip beamformer demo
- FieldTrip connectivity demo
- FieldTrip stats demo
- General instructions for MATLAB demo's
- Multimodal faces dataset
- SPM DCM demo
- SPM Sensor-level stats demo
- SPM Source reconstruction demo
- MEG/EEG analysis and FieldTrip Workshop in Oxford, UK
- MRC Partnership Grant FieldTrip workshop in Birmingham
- Neuroimaging II - Electrophysiological Methods
- Neuroimaging II - Electrophysiological Methods
- SED1 - Statistical testing of electrophysiological data
- SED2 - Statistical testing of electrophysiological data
- SED3 - Statistical testing of electrophysiological data
- SED4 - Statistical testing of electrophysiological data
- SPED1 - Signal processing of electrophysiological data
- SPED2 - Signal processing of electrophysiological data
- SPED3 - Signal processing of electrophysiological data
- SPED4 - Time-frequency analysis in practice using FieldTrip
- SR1 - Source reconstruction
- SR2 - Source reconstruction
- SR3 - Source reconstruction
- SR4 - Source reconstruction
- SR5 - Source reconstruction
- Neuroimaging II - Electrophysiological Methods
- SED1 - Statistical testing of electrophysiological data
- SED2 - Statistical testing of electrophysiological data
- SED3 - Statistical testing of electrophysiological data
- SED4 - Statistical testing of electrophysiological data
- SPED1 - Signal processing of electrophysiological data
- SPED2 - Signal processing of electrophysiological data
- SPED3 - Signal processing of electrophysiological data
- SPED4 - Time-frequency analysis in practice using FieldTrip
- SR1 - Source reconstruction
- SR2 - Source reconstruction
- SR3 - Source reconstruction
- SR4 - Source reconstruction
- SR5 - Source reconstruction
- PracticalMEEG workshop at ICM in Paris
- Creation of headmodels and sourcemodels for source reconstruction
- From raw data to ERP
- Group analysis
- Instructions for the USB stick
- Multimodal faces dataset
- Reconstructing source activity using beamformers
- Time-frequency analysis using Hanning window, multitapers and wavelets
- PracticalMEEG workshop in Aix-en-Provence
- Creation of headmodels and sourcemodels for source reconstruction
- Dealing with artifacts
- Group-level statistics with parametric and non-parametric methods
- Multimodal faces dataset
- Preprocessing raw data and computing ERPs/ERFs
- Reconstructing source activity using beamformers
- Time-frequency analysis using Hanning window, multitapers and wavelets
- PracticalMEEG workshop in Aix-en-Provence
- Creation of headmodels and sourcemodels for source reconstruction
- Dealing with artifacts
- Group-level statistics with parametric and non-parametric methods
- Multi-variate pattern analysis (MVPA)
- Multimodal faces dataset
- Preprocessing raw data and computing ERPs/ERFs
- Reconstructing source activity using beamformers
- Time-frequency analysis using Hanning window, multitapers and wavelets
- Realtime MEG BCI hands-on session in Nijmegen
- Summerschool on brain networks
- Toolkit of Cognitive Neuroscience - EEG/MEG
- Workshop in Tübingen on nonparametric statistics and connectivity
- Workshop on analyzing intracranial data - spikes and fields
- Workshop on the ECoG forward solution using FEMfuns in FieldTrip at the Donders
- Frequently asked questions
- Code and development questions
- How are the various MATLAB data structures defined?
- How can I debug my analysis script if a FieldTrip function gives an error?
- How can I keep track of changes to the code?
- What does a typical call to a FieldTrip function look like?
- Why was FieldTrip maintained in SVN and not in Git?
- Distributed computing questions
- How to compile MATLAB code into stand-alone executables?
- How to get started with distributed computing using qsub?
- How to get started with the MATLAB distributed computing toolbox?
- What are the different approaches I can take for distributed computing?
- Experimental questions
- How can I change the head localization in a CTF dataset?
- How can I monitor a subject's head position during a MEG session?
- How can I use my MacBook Pro for stimulus presentation in the MEG lab?
- How can I visualize the Neuromag head position indicator coils?
- What kind of cable do I need for a serial port connection between two computers?
- MATLAB questions
- Can I prevent "external" toolboxes from being added to my MATLAB path?
- Can I use FieldTrip without MATLAB license?
- Can I use Octave instead of MATLAB?
- How can I compile the mex files and command-line executables?
- How can I compile the mex files on 64-bit Windows?
- How can I compile the mex files on macOS?
- How many lines of code does FieldTrip consist of?
- How to select the correct SPM toolbox?
- Installation and setting up the path
- MATLAB complains about a missing or invalid mex file, what should I do?
- MATLAB complains that mexmaci64 cannot be opened because the developer cannot be verified
- MATLAB does not see the functions in the "private" directory
- MATLAB version 7.3 (2006b) crashes when I try to do ...
- Replacements for functions from MathWorks toolboxes
- The databrowser crashes and destroys the whole MATLAB session, how can I resolve this?
- What are the MATLAB and external requirements?
- What are the MATLAB requirements for using FieldTrip?
- Which external toolboxes are used by FieldTrip?
- Why are so many of the interesting functions in the private directories?
- Organizational questions
- Can I get an offline version of the documentation on the website?
- Can I use the FieldTrip logo on my poster?
- How many people are subscribed to the email discussion list?
- How should I refer to FieldTrip in my publication?
- How should I share example data with the email list or developers?
- How to ask good questions to the community?
- I am having problems downloading
- I am working at the Donders, should I also download FieldTrip?
- Which version of FieldTrip should I download?
- Why am I not allowed to post to the discussion list?
- Why am I not receiving emails from the discussion list?
- Why am I receiving warnings about too many bouncing emails?
- Why is FieldTrip developed separately from EEGLAB?
- Why is my message rejected from the email discussion list?
- Plotting and visualization questions
- How can I play back EEG/MEG and synchronous audio or video?
- How can I use the databrowser?
- How can I visualize a localspheres volume conductor model?
- How do I construct a layout file for the plotting functions?
- I am getting strange artifacts in figures that use opacity
- I am having problems printing figures that use opacity
- What are the different Neuromag/Elekta/Megin and Yokogawa layouts good for?
- What is a good way to save images for later processing in other software?
- What is the format of the layout file which is used for plotting?
- What is the plotting convention for anatomical MRIs?
- Which colormaps are supported?
- Why does my anatomical MRI show upside-down when plotting it with ft_sourceplot?
- Questions about reading and preprocessing of data
- Questions about artifacts
- Do I need to resample my data, and if so, how is this to be done?
- How can I consistently represent artifacts in my data?
- How can I interpret the different types of padding in FieldTrip?
- How does the filter padding in preprocessing work?
- I used ICA on my MEG data from before 2012 and now FieldTrip crashes, why is that?
- Should I rereference prior to or after ICA for artifact removal?
- What kind of filters can I apply to my data?
- Why does my ICA eyeblink component look strange?
- Why does my ICA output contain complex numbers?
- Why is there a residual 50Hz line-noise component after applying a DFT filter?
- Questions about data handling
- How can I append the files of two separate recordings?
- How can I convert one dataformat into an other?
- How can I merge two datasets that were acquired simultaneously with different amplifiers?
- How can I preprocess a dataset that is too large to fit into memory?
- How can I rename channels in my data structure?
- Reading is slow, can I write my raw data to a more efficient file format?
- What dataformats are supported?
- Questions about specific data formats
- How can I deal with a discontinuous Neuralynx LFP recording?
- How can I extend the reading functions with a new dataformat?
- How can I fix a corrupt CTF meg4 file?
- How can I fix a corrupt CTF res4 header file?
- How can I import my own data format?
- How can I inspect the electrode impedances of my BrainVision data?
- How can I read EGI mff data without the JVM?
- How can I read all channels from an EDF file that contains multiple sampling rates?
- How can I read corrupted (unsaved) CTF data?
- How does the CTF higher-order gradiometer work?
- I am having problems reading the CTF .hc headcoordinates file
- I have problems reading in NeuroScan .cnt files. How can I fix this?
- Why are the fileio functions stateless, does the fseek not make them very slow?
- Why should I set continuous to yes for CTF data?
- Why should I start with rereferencing for BioSemi EEG data?
- Questions about trials, triggers and events
- How can I check or decipher the sequence of triggers in my data?
- How can I find out what eventvalues and eventtypes there are in my data?
- How can I process continuous data without triggers?
- How can I transform trigger values from bits to decimal representation with a trialfun?
- Is it possible to keep track of trial-specific information in my analysis pipeline?
- What is the relation between "events" (such as triggers) and "trials"?
- Real-time analysis questions
- Does the FieldTrip realtime buffer only work with MATLAB?
- How fast is the FieldTrip buffer for realtime data streaming?
- How should I get started with the FieldTrip realtime buffer?
- Source reconstruction questions
- Can I do combined EEG and MEG source reconstruction?
- Can I restrict the source reconstruction to the grey matter?
- How are electrodes, magnetometers or gradiometers described?
- How are the Left and Right Pre-Auricular (LPA and RPA) points defined?
- How are the different head and MRI coordinate systems defined?
- How can I check whether the grid that I have is aligned to the segmented volume and to the sensor gradiometer?
- How can I convert an anatomical MRI from DICOM into CTF format?
- How can I determine the anatomical label of a source or electrode?
- How can I fine-tune my BEM volume conduction model?
- How can I map source locations onto an anatomical label in an atlas?
- How can I visualize the different geometrical objects that are needed for forward and inverse computations?
- How do I install the OpenMEEG binaries
- How do homogenous coordinate transformation matrices work?
- How is a segmentation defined?
- How is anatomical, functional or statistical "volume data" described?
- How should I report the positions of the fiducial points on the head?
- How should I specify the fiducials for electrode realignment?
- How to change the MRI orientation, the voxel size or the field-of-view?
- How to coregister an anatomical MRI with the gradiometer or electrode positions?
- Is it OK for vertices/dipoles to stick out of the volume conductor?
- Is it good or bad to have dipole locations outside of the brain for which the source reconstruction is computed?
- Is it important to have accurate measurements of electrode locations for EEG source reconstruction?
- My MRI is upside down, is this a problem?
- Should I use a Polhemus or a Structure Sensor to record electrode positions?
- What is the conductivity of the brain, CSF, skull and skin tissue?
- What is the difference between the ACPC, MNI, SPM and TAL coordinate systems?
- What kind of volume conduction models of the head (head models) are implemented?
- What material is used for the flexible MEG headcasts?
- Where can I find the dipoli command-line executable?
- Where is the anterior commissure?
- Why does my EEG headmodel look funny?
- Why is the source model deformed or incorrectly aligned after warping template?
- Why is there a rim around the brain for which the source reconstruction is not computed?
- Why should I use an average reference for EEG source reconstruction?
- Spectral analysis questions
- Does it make sense to subtract the ERP prior to time frequency analysis, to distinguish evoked from induced power?
- How can I compute inter-trial coherence?
- How can I do time-frequency analysis on continuous data?
- How does mtmconvol work?
- How to interpret the sign of the phase slope index?
- In what way can frequency domain data be represented in FieldTrip?
- What convention is used to define absolute phase in 'mtmconvol', 'wavelet' and 'mtmfft'?
- What does "padding not sufficient for requested frequency resolution" mean?
- What is meant by time-frequency trade off?
- What is the difference between coherence and coherency?
- Why am I not getting integer frequencies?
- Why do I need to run ft_freqanalysis before ft_combineplanar?
- Why does my TFR contain NaNs?
- Why does my TFR look strange (part I, demeaning)?
- Why does my TFR look strange (part II, detrending)?
- Why does my output.freq not match my cfg.foi when using 'mtmconvol' in ft_freqanalysis?
- Why does my output.freq not match my cfg.foi when using 'mtmfft' in ft_freqanalysis
- Why does my output.freq not match my cfg.foi when using 'wavelet' (formerly 'wltconvol') in ft_freqanalysis?
- Why is the largest peak in the spectrum at the frequency which is 1/segment length?
- Statistical questions
- How NOT to interpret results from a cluster-based permutation test
- How can I define neighbouring sensors?
- How can I determine the onset of an effect?
- How can I test an interaction effect using cluster-based permutation tests?
- How can I test for correlations between neuronal data and quantitative stimulus and behavioral variables?
- How can I test whether a behavioral measure is phasic?
- How can I use the ivar, uvar, wvar and cvar options to precisely control the permutations?
- How does a difference in trial numbers per condition affect my statistical test
- How does ft_prepare_neighbours work?
- Should I use t or F values for cluster-based permutation tests?
- What is the idea behind statistical inference at the second-level?
- Why are there multiple neighbour templates for the Neuromag306 system?
- Why should I use the cfg.correcttail option when using statistics_montecarlo?
- Various other questions
- Are the FieldTrip lectures available on video?
- Can I compare EEG channels between different electrode caps?
- Can I organize my own FieldTrip workshop?
- How can I anonymize or deidentify DICOM files?
- How can I anonymize or deidentify a BrainVision dataset?
- How can I anonymize or deidentify a CTF dataset?
- How can I anonymize or deidentify an anatomical MRI?
- How can I anonymize or deidentify data processed in FieldTrip?
- How can I share my MEG data?
- How do I prevent FieldTrip from printing the time and memory after each function call?
- How should I prepare for the upcoming FieldTrip workshop?
- How should I specify the coordinate systems in a BIDS dataset?
- What are the units of the data and of the derived results?
- Where can I find open access MEG/EEG data?
- Where can I find open hardware for MEG and EEG?
- Which datasets are used in the documentation and where are they used?
- Which methodological details should I report in an EEG/MEG manuscript?
- Which version of FieldTrip should I download?
- Getting started
- Getting started with (data from) other software
- Getting started with AnyWave
- Getting started with BIDS
- Getting started with BioImage Suite
- Getting started with EEGLAB
- Getting started with Homer
- Getting started with LIMO MEEG
- Getting started with LORETA
- Getting started with MNE-Python
- Getting started with MeshLab
- Getting started with ParaView
- Getting started with SPM
- Getting started with Seg3D
- Getting started with SimNIBS
- Getting started with NIRS data
- Getting started with Artinis NIRS data
- Getting started with Hitachi NIRS data
- Getting started with NIRx NIRS data
- Getting started with SNIRF data
- Getting started with Shimadzu NIRS data
- Getting started with eyetracker data
- Getting started with intracranial data
- Getting started with Blackrock data
- Getting started with Cyberkinetics data
- Getting started with Neuralynx data
- Getting started with Neuralynx data recorded at the Donders Institute
- Getting started with Neurodata Without Borders (NWB) data
- Getting started with Plexon data
- Getting started with animal electrophysiology data, including spikes
- Getting started with human ECoG data
- Getting started with motion capture data
- Getting started with other types of data
- Getting started with audio data
- Getting started with fMRI timeseries data
- Getting started with video data
- Getting started with particular EEG data types
- Getting started with 20%, 10% and 5% electrode arrangements
- Getting started with ABM's B-Alert EEG data
- Getting started with ANT-Neuro, ASA and EEProbe data
- Getting started with BESA data
- Getting started with BioSemi BDF data
- Getting started with BrainVision Analyzer and Easycap
- Getting started with EDF (European Data Format) data
- Getting started with EGI/Philips/Magstim data
- Getting started with Nicolet data
- Getting started with TMSi data
- Getting started with particular MEG data types
- Getting started with BTi/4D data
- Getting started with BabySQUID data
- Getting started with CTF data
- Getting started with Cerca OPM data
- Getting started with FieldLine OPM data
- Getting started with Neuromag/Elekta/Megin data
- Getting started with OPM data recorded at the FIL
- Getting started with QuSpin OPM data
- Getting started with Ricoh data
- Getting started with Yokogawa data
- Getting started with real-time analysis
- Index of all configuration options
- Pagelist
- Privacy Policy
- Reference documentation
- References to implemented methods
- Review papers and teaching material
- Search results
- Support
- Template models and data
- Anatomical templates for visualizing source reconstructed activity
- Template 2-D layouts for plotting
- Template 3-D electrode sets
- Template 3-D gradiometer descriptions
- Template MEG dewar shapes
- Template anatomical atlases and parcellation schemes
- Template head models for EEG volume conduction modeling
- Template models for source reconstruction
- Templates for defining neighbouring channels
- Tutorials
- Details of the MEG language dataset
- Details of the tMEG Motor task dataset
- Details on the EEG language dataset
- Details on the multimodal faces dataset
- Tutorials on connectivity analysis
- Analysis of corticomuscular coherence
- Analysis of sensor- and source-level connectivity
- Connectivity in auditory evoked responses
- Extended analysis of sensor- and source-level connectivity
- Whole brain connectivity and network analysis
- Whole brain connectivity and network analysis
- Whole brain connectivity and network analysis
- Tutorials on making your analyses more efficient
- Making a memory efficient analysis pipeline
- Speeding up your analysis using distributed computing with parfor
- Speeding up your analysis using distributed computing with qsub
- Tutorials on plotting and visualization
- Tutorials on reading and preprocessing of data
- Automatic artifact rejection
- Cleaning artifacts using ICA
- Introduction on dealing with artifacts
- Preprocessing - Reading continuous EEG and MEG data
- Preprocessing - Segmenting and reading trial-based EEG and MEG data
- Visual or manual artifact rejection
- Tutorials on sensor-level analyses
- Designing a custom 3D printed OPM helmet
- Event-related averaging and MEG planar gradient
- Extracting the brain state and events from continuous sleep EEG
- Preprocessing and event-related activity in combined MEG/EEG data
- Preprocessing of EEG data and computing ERPs
- Preprocessing of Optically Pumped Magnetometer (OPM) data
- Sensor-level ERF, TFR and connectivity analyses
- Time-frequency analysis of combined MEG/EEG data
- Time-frequency analysis using Hanning window, multitapers and wavelets
- Tutorials on source reconstruction
- Beamforming oscillatory responses in combined MEG/EEG data
- Computation of virtual MEG channels in source-space
- Coregistration of Optically Pumped Magnetometer (OPM) data
- Creating a BEM volume conduction model of the head for source reconstruction of EEG data
- Creating a FEM volume conduction model of the head for source reconstruction of EEG data
- Creating a source model for source reconstruction of MEG or EEG data
- Creating a volume conduction model of the head for source reconstruction of MEG data
- Dipole fitting of combined MEG/EEG data
- EEG headmodels
- Localizing electrodes using a 3D-scanner
- Localizing oscillatory sources using beamformer techniques
- Localizing sources using beamformer techniques
- Localizing visual gamma and cortico-muscular coherence using DICS
- Source reconstruction of event-related fields using minimum-norm estimation
- Virtual channel analysis of epilepsy MEG data
- Tutorials on statistical analysis
- Classification of event-related MEG data using MVPA-Light
- Cluster-based permutation tests on event-related fields
- Cluster-based permutation tests on time-frequency data
- Parametric and non-parametric statistics on event-related fields
- Statistical analysis and multiple comparison correction for combined MEG/EEG data
- Tutorials on the analysis of NIRS data
- Preprocessing and averaging of multi-channel NIRS data
- Preprocessing and averaging of single-channel NIRS data
- Tutorials on the analysis of TMS data
- Tutorials on the 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 and local field potential data
- Preprocessing and analysis of spike-train data
- Tutorials that introduce FieldTrip and MATLAB
- Video lectures and tutorials
- Welcome to the FieldTrip website