Installation

Pick a language below — Case Study 1 and 2 run independently in each one, so you only need to set up the one you plan to use.

Each case study's code page offers both a Jupyter notebook (handy for developing and debugging interactively) and a plain script with the same code, for running without Jupyter at all. The Jupyter kernel steps below are only needed for the notebook form.

Python R Matlab

Covers Case Study 1 and 2:

terminal

conda create -n eegnpsi python=3.11
conda activate eegnpsi
pip install -r python/requirements.txt

This installs pandas, numpy, scipy, statsmodels, matplotlib, seaborn, mne (Case Study 2's montage/cluster-permutation analysis), and openpyxl (Excel export).

Both case studies expect a Jupyter kernel named eegnpsi:

terminal

python -m ipykernel install --user --name eegnpsi --display-name "Python (eegnpsi)"

Covers Case Study 1 and 2. You'll need R ≥ 4.4:

terminal

Rscript r/requirements.R

r/requirements.R is the R equivalent of python/requirements.txt — a plain package list, see the file itself for what each one is for.

Then register the Jupyter kernel (one-time setup, not a package):

R console

IRkernel::installspec(name = "ir_eegnpsi", displayname = "R (eegnpsi)")

See Case Study 1's and Case Study 2's R code for working examples.

Not implemented yet — see Contributing if you'd like to help bring Case Study 1 or 2 to Matlab.

Documentation site (local preview)

Only needed if you're editing this website itself, not for running the case studies:

terminal

pip install -r website/docs/requirements.txt
cd website && mkdocs serve