AI RESEARCH
Riemannian Geometry-Preserving Variational Autoencoder for MI-BCI Data Augmentation
arXiv CS.LG
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ArXi:2603.10563v1 Announce Type: new This paper addresses the challenge of generating synthetic electroencephalogram (EEG) covariance matrices for motor imagery brain-computer interface (MI-BCI) applications. Objective: We aim to develop a generative model capable of producing high-fidelity synthetic covariance matrices while preserving their symmetric positive-definite nature.