Offline decoding of motor intent from EEG. Right now we are in the decoding phase: reading the literature to learn which decoding methods work best for intent, and testing those methods in Python on offline data. Later we want to branch out into hardware: a BCI optimized for dry-electrode, offline decoding.
| Section | What lives there |
|---|---|
| literature/ | One note per paper, plus a summary table |
| experiments/ | One folder per experiment, plus an index table |
| src/ | Reusable code promoted from experiments |
| docs/decisions.md | Short log of "we chose X because Y" |
- Literature review started
- Baseline experiment (exp001) done
- Methods compared under one evaluation protocol
- Hardware requirements drafted
- Read a paper, then add a note in
literature/papers/usingliterature/_TEMPLATE.md. - Test the method: copy
experiments/_TEMPLATE/to a new numbered folder. - Log the result in the experiments index and link it back to the paper.
- Record big choices in
docs/decisions.md.
(Add environment setup steps here, e.g. Python version and pip install -r requirements.txt.)