aristotelisballas/biodg

This project helps biomedical researchers and data scientists evaluate how well their machine learning models for biosignal classification perform across different datasets or 'domains'. It takes various raw biosignal data, specifically Electrocardiogram (ECG) and Electroencephalogram (EEG) recordings, along with associated clinical labels. The output is a performance evaluation of different domain generalization algorithms, showing how robust models are when applied to new, unseen patient populations or recording conditions. Researchers working with physiological signals and machine learning for diagnostic or monitoring applications would use this.