用几何保持方法生成高质量脑电协方差矩阵，提升脑机接口数据量。
Riemannian Geometry-Preserving Variational Autoencoder for MI-BCI Data Augmentation
- 基于黎曼几何设计变分自编码器，保持协方差矩阵的正定性。
- 生成的合成数据在不同分类器上表现良好，有效提升模型性能。
- 适合需要隐私保护和数据增强的脑机接口研究者使用。
本文针对运动想象脑机接口（MI-BCI）中生成合成脑电（EEG）协方差矩阵的挑战，提出一种保留黎曼几何特性的变分自编码器（RGP-VAE）。该模型通过几何映射与复合损失函数（包含黎曼距离、切空间重建精度及生成多样性）联合优化，生成符合对称正定性质的高保真合成协方差矩阵，并学习到跨被试共享的潜在空间。实验表明，合成数据在实际MI-BCI应用中具有实用性，其效果依赖于所配对的分类器。本工作验证了RGP-VAE作为几何保持型生成模型的有效性，展现出在信号隐私保护、系统可扩展性和数据增强方面的潜力。
原文摘要 · Abstract (English)
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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