Riemannian Geometry-Preserving Variational Autoencoder for MI-BCI Data Augmentation

• URL: http://arxiv.org/abs/2603.10563v1
• Date: Wed, 11 Mar 2026 09:13:34 GMT
• Title: Riemannian Geometry-Preserving Variational Autoencoder for MI-BCI Data Augmentation
• Abstract summary: This paper addresses the challenge of generating synthetic electroencephalogram (EEG) covariance matrices for motor imagery brain-computer interface (MI-BCI) applications.<n>We propose a geometry-preserving variational autoencoder (RGP-VAE) integrating geometric mappings with a composite loss function.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. Approach: We propose a Riemannian geometry-preserving variational autoencoder (RGP-VAE) integrating geometric mappings with a composite loss function combining Riemannian distance, tangent space reconstruction accuracy and generative diversity. Results: The model generates valid, representative EEG covariance matrices, while learning a subject-invariant latent space. Synthetic data proves practically useful for MI-BCI, with its impact depending on the paired classifier. Contribution: This work introduces and validates the RGP-VAE as a geometry-preserving generative model for EEG covariance matrices, highlighting its potential for signal privacy, scalability and data augmentation.

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