Start here

After this module you will know what the next two weeks look like, have a working Python environment that can open the course's EEG files, and have started the notes file you will bring to your first meeting with Alex.

What this course gets you #

Two weeks, about six hours of focused work in total, taking you from zero to three concrete abilities:

1. Read an EEG trace. Look at a raw recording and say where the signal comes from, which rhythm you are seeing, which electrode it is on, and whether a bump is brain or artifact.
2. Explain what is in it. Use the right vocabulary: bands, montages, references, artifacts, band power, reward and inhibit thresholds, RMSSD.
3. Run a basic analysis in Python. Load an EDF, filter it, remove a blink with ICA, and compute band power per channel with MNE-Python.

Everything here is free. The only install is Python and a few open-source libraries.

How to use it #

• Week 1 before Week 2. Each module builds on the previous one, and the two labs reuse each other's data.
• Sessions are 30-45 minutes. The two hands-on labs (Lab 1 and Lab 2) want a dedicated 60-90 minute block.
• You do not need every link. Each module says what to read, watch or run. The resource library is the longer menu for when you want more.
• Pair up. Hands-on sessions go faster with one person driving the keyboard and the other reading the docs.
• The day-by-day schedule maps modules to dates; the checkpoints list is what you should be able to do before the first meeting.

The course map #

flowchart LR
  subgraph W1["Week 1: what EEG is"]
    M00["00 Start here"] --> M01["01 Where the signal<br/>comes from"]
    M01 --> M02["02 Frequency bands"]
    M02 --> M03["03 Electrodes<br/>and montages"]
    M03 --> M04["04 Reading a<br/>raw trace"]
    M04 --> M05["05 Artifacts<br/>field guide"]
    M05 --> M06["06 Lab 1: find<br/>the alpha peak"]
  end
  subgraph W2["Week 2: working with the signal"]
    M07["07 Filtering"] --> M08["08 Re-referencing"]
    M08 --> M09["09 Artifact handling:<br/>rejection, ICA, ASR"]
    M09 --> M10["10 Spectral features"]
    M10 --> M11["11 Epochs and ERPs"]
    M11 --> M12["12 Lab 2: clean<br/>and measure"]
    M12 --> M13["13 The neurofeedback<br/>loop"]
    M13 --> M14["14 HRV basics"]
    M14 --> M15["15 Divergence<br/>orientation"]
  end
  M06 --> M07

The Week 2 pipeline (filter, re-reference, remove artifacts, compute features) is the same one, in the same order, that nearly every EEG tool runs, including the Divergence platform.

Set up Python #

You need Python 3.10 or newer. The standard EEG library in Python is MNE-Python: free, well documented, and what every hands-on part of this course uses. The MNE install guide has the full story; the short version is a fresh virtual environment.

From the root of this repository:

python3 -m venv .venv
source .venv/bin/activate          # Windows: .venv\Scripts\activate
pip install -r code/requirements.txt

That installs MNE, NumPy, SciPy, Matplotlib, Jupyter, an EDF reader, and two optional extras used later (mne-icalabel in Module 09 and NeuroKit2 in Module 14).

Check it works:

cd code
python -c "from eeg101 import load_sample; r = load_sample('rest_ec'); print(r)"

You should see something like <RawEDF | s01_rest_eyes-closed_19ch.edf, 19 x 77500 (310.0 s), ~11.3 MiB, data loaded>.

Use VS Code or JupyterLab, whichever you already know. If you know neither, JupyterLab (jupyter lab) is the gentler start: you can run one line at a time and see each plot as you go.

What is in this repository #

The notes file habit #

Keep a running notes file. Markdown in the project GitHub repo is ideal; copy notes-template.md to start. Every session, add:

• Every term you had to look up, with a one-line definition in your own words.
• Every question you want to ask Alex. No question is too basic; write it down while it is fresh.
• Screenshots and plots from the labs, with a sentence on what each shows.
• Anything that surprised you. Surprises are where the understanding is.

Bring that file to the first meeting. It turns the meeting from a lecture into a conversation about what you actually found hard.

Self-check #

Next #

Module 01: Where the signal comes from