In recent years, Transformer-based auto-attention mechanisms have been successfully applied to the analysis of a variety of context-reliant data types, from texts to images and beyond, including data from non-Euclidean geometries. In this paper, we present such a mechanism, designed to classify sequences of Symmetric Positive Definite matrices while preserving their Riemannian geometry throughout the analysis. We apply our method to automatic sleep staging on timeseries of EEG-derived covariance matrices from a standard dataset, obtaining high levels of stage-wise performance.

翻译：近年来，基于Transformer的自注意力机制已成功应用于多种依赖上下文的数据类型分析，从文本到图像等领域，包括非欧几里得几何数据。本文提出了一种在分析过程中保持对称正定矩阵黎曼几何结构的分类机制，并应用于标准数据集中脑电图协方差矩阵时间序列的自动睡眠分期，取得了高水平的阶段性能。