fieldtrip/fieldtrip

The MATLAB toolbox for MEG, EEG and iEEG analysis

What it solves

FieldTrip provides a comprehensive environment for analyzing electrophysiological data from MEG, EEG, and iEEG. It addresses the difficulty of processing complex brain-signal data across different hardware systems and implementing advanced mathematical analysis methods.

How it works

It is a MATLAB toolbox that provides a library of high-level functions. These functions allow researchers to build custom analysis protocols and easily integrate new analysis methods. The toolbox supports a wide variety of data formats from major MEG and EEG systems (such as CTF, Neuromag, and BTi/4D).

Who it’s for

Researchers in neuroscience and electrophysiology who need to analyze brain activity data using MATLAB.

Highlights

  • Advanced analysis methods including time-frequency analysis and non-parametric statistical testing.
  • Source reconstruction capabilities using dipoles, distributed sources, and beamformers.
  • Broad compatibility with data formats from all major MEG and EEG systems.
  • Extensible architecture that allows methods researchers to incorporate new analysis techniques.

Related

  • Project
    braindecode/braindecode

    Braindecode is a Python toolbox that brings deep‑learning models to raw brain‑signal data (EEG/ECoG/MEG). It provides dataset loaders, preprocessing, several ready‑made neural‑network architectures, and visualisation utilities, all built on top of PyTorch and MNE‑Python. Installable via `pip install braindecode`, it targets neuroscientists and ML researchers working with neurophysiological data.

  • Project
    mne-tools/mne-python

    An open-source Python package for exploring, visualizing, and analyzing human neurophysiological data like MEG and EEG.

  • Project
    embeddings-benchmark/mteb

    A multimodal toolbox for evaluating embeddings and retrieval systems, providing a standardized benchmark and leaderboard for comparing model performance.

  • Project
    orfeotoolbox/OTB

    An open-source library for processing high-resolution remote sensing images, capable of handling terabyte-scale optical, multispectral, and radar data.

  • Project
    nolabs-ai/deepfabric

    A synthetic data generation and evaluation framework that creates high-quality training datasets for agentic LLMs using real tool execution and topic graph mapping.