@tommoral.bsky.social @agramfort.bsky.social

7/ GOPSA offers a robust solution for addressing joint (X, y) shifts in EEG data, significantly improving model generalization. It integrates Riemannian geometry with mixed-effects modeling, providing novel tools for generating neuroscientific and clinical insights.

6/ 🎯 Re-centering sites helped reduce the shift in X, while not placing all sites at the exact same reference point helped manage the shift in y. This preserves the statistical associations between X and y, improving model generalization.

5/ 📊 Our benchmark on the HarMNqEEG dataset showed significant improvement in performance for three metrics across most site combinations of GOPSA compared to baseline methods.

4/ 📊 Results on simulated data show how GOPSA adapts to various degrees of shifts in X and y. Panel C illustrates that GOPSA is specifically designed to handle joint shifts in X and y.

3/💡Our method, GOPSA, jointly learns parallel transport along a geodesic for each domain and a global regression model common to all domains, assuming that the mean y can be estimated.

2/ Multicenter neuroscience datasets often face shifts in both feature (X) and label (y) distributions. Our work specifically addresses cases where features are covariance matrices and focuses on joint (X, y) dataset shifts in EEG data.


@rflamary.bsky.social @ambroiseodt.bsky.social shared feelings 😅