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cs.LG2026
Riemannian Geometry-Preserving Variational Autoencoder for MI-BCI Data Augmentation
Viktorija Poļaka, Ivo Pascal de Jong, Andreea Ioana Sburlea
This paper addresses the challenge of generating synthetic electroencephalogram (EEG) covariance matrices for motor imagery brain-computer interface (MI-BCI) applications. Objectiv…
cs.LG2026
The Challenge of Out-Of-Distribution Detection in Motor Imagery BCIs
Merlijn Quincent Mulder, Matias Valdenegro-Toro, Andreea Ioana Sburlea +1
Machine Learning classifiers used in Brain-Computer Interfaces make classifications based on the distribution of data they were trained on. When they need to make inferences on sam…