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Papers/Structure-Preserving Transformers for Sequences of SPD Mat...

Structure-Preserving Transformers for Sequences of SPD Matrices

Mathieu Seraphim, Alexis Lechervy, Florian Yger, Luc Brun, Olivier Etard

2023-09-14Sleep StagingSleep Stage DetectionEEG
PaperPDFCodeCode(official)

Abstract

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.

Results

TaskDatasetMetricValueModel
Sleep QualityMASS SS3Macro-F10.8124SPDTransNet
Sleep Stage DetectionMASS SS3Macro-F10.8124SPDTransNet

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