Interpreting the latent space of a Convolutional Variational Autoencoder for semi-automated eye blink artefact detection in EEG signals

Electroencephalography (EEG) allows the investigation of brain activity. However, neural signals often contain artefacts, hindering signal analysis. For example, eye-blink artefacts are particularly challenging due to their frequency overlap with neural signals. Artificial intelligence, particularly...

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Bibliographic Details

Published in:	Computer standards and interfaces Vol. 92; p. 103897
Main Authors:	Criscuolo, Sabatina, Apicella, Andrea, Prevete, Roberto, Longo, Luca
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.03.2025
Subjects:	Convolution Electroencephalography Latent space interpretation Ocular artefacts detection Variational autoencoders Variational autoencoders Electroencephalography Convolution Latent space interpretation Ocular artefacts detection
ISSN:	0920-5489
Online Access:	Get full text
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