IEEE Transactions on Biomedical Engineering· 2025
Adaptive Riemannian manifold learning for cross-session motor imagery BCI calibration
A. Barachant, S. Bonnet, M. Congedo, C. Jutten
Significance
For: developer
The problem
EEG non-stationarity across sessions and days degrades motor-imagery classifier accuracy, requiring tedious recalibration.
Method
Covariance matrices projected onto the Riemannian manifold tangent space with geodesic parallel transport and affine-invariant divergence.
Result
Reduced cross-session calibration time by 65% while improving Cohen's kappa from 0.58 to 0.74 across BCI Competition IV benchmarks.
Why it matters
Provides a mathematically rigorous, zero-hyperparameter preprocessing foundation now widely adopted in real-time pipelines.
Limitations
Computationally sensitive to rank-deficient covariance estimates when channel counts exceed sample windows.
Motor ImageryRiemannian GeometryDSPMachine Learning