Ambulatory EEG Subset for Stroke & Seizure Machine Learning Pipeline
This repository contains a 12-file subset of the CHB-MIT Scalp EEG Database (PhysioNet) used to evaluate adaptive noise filtering algorithms and machine learning classification for ambulatory EEG signal processing. The dataset includes 12 pre-packaged '.edf' files spanning 6 subjects (2 files per subject, including baseline non-seizure EEG and annotated seizure events): - Subject 01: chb01_01.edf, chb01_02.edf - Subject 02: chb02_01.edf, chb02_02.edf - Subject 03: chb05_01.edf, chb05_02.edf - Subject 04: chb08_02.edf, chb08_03.edf - Subject 05: chb13_05.edf, chb13_06.edf - Subject 06: chb14_01.edf, chb14_02.edf The file 'eeg_data.zip' contains all 12 raw '.edf' files compressed for high-speed retrieval in Google Colab notebooks via 'wget'. Original data provided by the CHB-MIT Scalp EEG Database on PhysioNet, licensed under Open Data Commons Attribution License v1.0 (ODC-By v1.0). Citations: - Guttag, J. (2010). CHB-MIT Scalp EEG Database (version 1.0.0). PhysioNet. RRID:SCR_007345. https://doi.org/10.13026/C2K01R - Shoeb, Ali Hossam. (2009). Application of machine learning to epileptic seizure onset detection and treatment. Mit.Edu. https://dspace.mit.edu/entities/publication/60e3e3f8-4cbb-4f41-8577-34bbedab9aba - Pollard, T., Moody, B. E., Lehman, L., Gow, B., Fernandes, C., Xie, C., Johnson, A., Mark, R. G., & Heldt, T. (2026). PhysioNet as a global platform for biomedical research. Nature Health. https://doi.org/10.1038/s44360-026-00096-z