FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data - PubMed (original) (raw)

FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data

Robert Oostenveld et al. Comput Intell Neurosci. 2011.

Abstract

This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time-frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level. The implementation as toolbox allows the user to perform elaborate and structured analyses of large data sets using the MATLAB command line and batch scripting. Furthermore, users and developers can easily extend the functionality and implement new algorithms. The modular design facilitates the reuse in other software packages.

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Figures

Figure 1

Figure 1

The structure of the toolbox.

Figure 2

Figure 2

Data representation examples. (a) Epoched time domain, sensor-level data. (b) Time-frequency representation of sensor-level data.

Figure 3

Figure 3

Example analysis pipeline.

Figure 4

Figure 4

Example analysis script.

Figure 5

Figure 5

Visualization of data with the ft_databrowser function. (a) Display of a set of sensors in the “butterfly” mode, showing the possibility to select segments of interest, for example, to identify artifacts (pink box). (b) Display of a set of sensors in the “vertical” mode.

Figure 6

Figure 6

Visualization of source-reconstructed data. (a) Three-dimensional orthographic rendering of corticomuscular coherence with opacity mapping. (b) Surface rendering of statistically thresholded corticomuscular coherence after Z-transformation.

Figure 7

Figure 7

Visualization of multidimensional data. (a) Topographical representation of a specific temporal component of the ERF. (b) Single sensor display of an ERF. (c) Topographical display of sensor-level ERFs in three experimental conditions. (d) Topographical display of sensor-level TRFs.

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