@Article{RI-Seraphim-2024,


author = {Mathieu Seraphim and Alexis Lechervy and Florian Yger and Luc Brun and Olivier Etard},
title = {Automatic Classification of Sleep Stages from EEG Signals Using Riemannian Metrics and Transformer Networks},
journal = {Springer Nature Computer Science},
year = 2024,
month="Oct",
volume="5",
number="7",
pages="953",
theme = "pattern",
abstract="In sleep medicine, assessing the evolution of a subject’s sleep often involves the costly manual scoring of electroencephalographic (EEG) signals. In recent years, a number of Deep Learning approaches have been proposed
to automate this process, mainly by extracting features from said signals.
However, despite some promising developments in related problems, such as
Brain-Computer Interfaces, analyses of the covariances between brain regions
remain underutilized in sleep stage scoring. Expanding upon our previous work, we investigate the capabilities of SPDTransNet, a Transformer-derived network designed to classify sleep stages from EEG data through timeseries of covariance matrices. Furthermore, we present a novel way of integrating learned signal-wise features into said matrices without sacrificing their Symmetric Definite Positive (SPD) nature. Results: Through comparison with other State-of-the-Art models within a methodology optimized for class-wise performance, we achieve a level of performance at or beyond various State-of-the-Art models, both in single-dataset and - particularly - multi-dataset experiments. In this article, we prove the capabilities of our SPDTransNet model, particularly its adaptability to multi-dataset tasks, within the context of EEG sleep stage scoring - though it could easily be adapted to any classification task involving timeseries of covariance matrices.",
url="SNSharedIt:=https://rdcu.be/dWNI2, HAL:=https://hal.science/hal-04638612v1"

 }