To participate in the workshop, you will need to set up a Python environment with a number of dependencies. The following instructions will guide you through the process.

The notebooks will be made available in this GitHub repository at the beginning of the workshop.

We will work with the Anaconda Python distribution.

• Installation instructions for Windows
• Installation instructions for macOS
• Installation instructions for Linux

• Open an Anaconda prompt
• Follow the MNE-Python installation instructions for your operating system
• Activate your newly-installed MNE-Python environment by typing into the Anaconda prompt:

  conda activate mne
  

• Verify your MNE-Python installation by running in the Anaconda prompt:

  mne sys_info
  

  The output should look similar to the following:

    Platform:      macOS-10.15.7-x86_64-i386-64bit
    Python:        3.8.5 (default, Sep  4 2020, 02:22:02)  [Clang 10.0.0 ]
    Executable:    /Users/richard/miniconda3/envs/mne-new/bin/python
    CPU:           i386: 4 cores
    Memory:        8.0 GB
  
    mne:           0.21.1
    numpy:         1.19.2 {blas=mkl_rt, lapack=mkl_rt}
    scipy:         1.5.2
    matplotlib:    3.3.2 {backend=MacOSX}
  
    sklearn:       0.23.2
    numba:         0.51.2
    nibabel:       3.2.0
    cupy:          Not found
    pandas:        1.1.3
    dipy:          1.3.0
    mayavi:        4.7.2
    pyvista:       0.27.2 {pyvistaqt=0.2.0, OpenGL 4.1 INTEL-14.7.8 via Intel HD Graphics 5000 OpenGL Engine}
    vtk:           9.0.1
    PyQt5:         5.13.2
  

We will use the Jupyter Lab environment to run our workshop notebooks. Inside the Anaconda prompt, run:

conda activate mne
conda install -y jupyterlab

Then, run:

jupyter lab

If the installation was successful, your browser should open. You may close it again, and stop the JupyterLab process in the Anaconda prompt by pressing the keys Ctrl and C simultaneously and then enterying y when asked if you're sure.

To readand write BIDS data, we will use MNE-BIDS. To install it, run the following commands inside the Anaconda prompt:

conda activate mne
pip install mne-bids nibabel pybv

We will work with some sample data that can be easily retrieved via MNE-Python. Open an Anaconda prompt and run:

conda activate mne
python -c "import mne; mne.datasets.sample.data_path()"

This will retrieve about 1.5 GB of data, so it might take a while if your internet connection is slow.

The workshop will cover a broad range of topics to help you get to know all essential parts of MNE-Python for conducting MEG and EEG data analysis:

• loading, filtering, and inspecting raw data
• working with BIDS data
• epoching and artifact correction
• creating and visualizing evoked responses (ERP / ERF)
• contrasting evoked responses of different experimental conditions
• decoding neural responses (machine learning)
• performing time-frequency analysis
• estimating and visualizing cortical sources (source localization)
• conducting a group analysis

We will start with a 30 min warm-up at 9:00 a.m. UTC on both days. During this warm-up, we can resolve technical difficulties you might be experiencing. The actual workshop will start at 9:30.

Lunch break is from 12 to 1, and we will continue until 3:30 or longer if need be (to hopefully address all open questions).