Canonical EEG representation framework
Project description
EEGCanon-ML 🧠⚡
EEGCanon-ML is a Python framework that converts heterogeneous EEG data (EDF, CSV) into a canonical, standardized representation for reproducible machine learning and signal analysis.
It provides a common EEG data language so that models, features, and experiments can be compared fairly across datasets.
Why EEGCanon-ML?
EEG workflows today suffer from:
- dataset-specific preprocessing scripts
- inconsistent channel naming
- incompatible sampling rates
- poor reproducibility across studies
EEGCanon-ML enforces a standard EEG contract.
Key Features
- Unified EEG loading (EDF, CSV)
- Canonical 10-20 channel mapping
- Sampling-rate normalization
- Structured warnings & provenance tracking
- Epoching support
- Feature extraction (PSD, Bandpower, Hjorth)
- PyTorch-ready datasets
Installation
pip install eegcanon-ml
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