EEGNetReplication data-science pipeline

Replication of results of the original EEGNet paper. We are focused on the SMR test replication specifically

In this project following steps were done for the replication process:

• fetches data: BCI Competition IV; Dataset 2a,
• pre-process fetched data,
• trains a classifier (CNN Model) to predict SMR actions,
• logs everything to app.log.

Quickstart

python -m venv .eegnetenv
# Windows: .eegnetenv\Scripts\activate
# macOS/Linux: source .eegnetenv/bin/activate

pip install -e ".[ds,test,lint]"

1. Fetch data (cached into data/raw/) from kaggle

python -m eegnet_repl.fetch --src kaggle

Alternative:

python -m eegnet_repl.fetch --src moabb

2. Preprocess data (cached into data/processed/)

python -m eegnet_repl.dataset --src kaggle

3. Train model & Report generation

python -m eegnet_repl.train --trainingType Within-Subject --generateReport True

4. Run UI

python -m eegnet_repl.ui

Unit tests

1. Unit test for functions in dataset.py

python -m pytest tests/test_dataset.py -v

2. Unit test for functions in model.py

python -m pytest tests/test_model.py -v