Temporal Sequences of EEG Covariance Matrices for Automated Sleep Stage Scoring with Attention Mechanisms

Seraphim, Mathieu; Dequidt, Paul; Lechervy, Alexis; Yger, Florian; Brun, Luc; Etard, Olivier

doi:10.1007/978-3-031-44240-7_7

Lecture Notes in Computer Science

2023

DOI: 10.1007/978-3-031-44240-7_7

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Temporal Sequences of EEG Covariance Matrices for Automated Sleep Stage Scoring with Attention Mechanisms

Mathieu Seraphim,

Paul Dequidt,

Alexis Lechervy

et al.

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2024

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Cited by 1 publication

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Automatic Classification of Sleep Stages from EEG Signals Using Riemannian Metrics and Transformer Networks

Seraphim,

Lechervy,

Yger

et al. 2024

SN COMPUT. SCI.

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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. Methods: 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. Conclusion: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.

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Automatic Classification of Sleep Stages from EEG Signals Using Riemannian Metrics and Transformer Networks

Seraphim,

Lechervy,

Yger

et al. 2024

SN COMPUT. SCI.

View full text Add to dashboard Cite

show abstract

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Temporal Sequences of EEG Covariance Matrices for Automated Sleep Stage Scoring with Attention Mechanisms

Cited by 1 publication

References 17 publications

Automatic Classification of Sleep Stages from EEG Signals Using Riemannian Metrics and Transformer Networks

Automatic Classification of Sleep Stages from EEG Signals Using Riemannian Metrics and Transformer Networks

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