Abstract / truncated to 115 words (read the full abstract)

The scoring of a subject's sleep stages from electroencephalographic (EEG) signals is a costly process. As such, many approaches to its automation have been proposed, including ones based on Deep Learning. However, said approaches have yet to attain a level of performance good enough for use in clinical settings, in part due to the high variability between EEG recordings, and the challenges inherent to the classification of signals recorded in different environments.In this thesis, we tackle this issue through a novel angle, by representing each epoch within our EEG signals as a timeseries of covariance matrices. Said matrices, although a common tool for EEG analysis in Brain-Computer Interfaces (BCI), are not typically utilized in sleep ... toggle 5 keywords

sleep scoring – electroencephalography – covariance matrices – riemannian manifold – transformers

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