Subject Representation Learning from EEG using Graph Convolutional Variational Autoencoders

We propose GC-VASE, a graph convolutional-based variational autoencoder that leverages contrastive learning for subject representation learning from EEG data. Our method successfully learns robust subject-specific latent representations using the split-latent space architecture tailored for subject...

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Veröffentlicht in:	Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) S. 1 - 5
Hauptverfasser:	Mishra, Aditya, Samin, Ahnaf Mozib, Etemad, Ali, Hashemi, Javad
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 06.04.2025
Schlagworte:	Accuracy Adaptation models Adapters Autoencoders Brain modeling Computational modeling Contrastive learning EEG Electroencephalography GNNs Medical diagnosis Representation learning Speech processing
ISSN:	2379-190X
Online-Zugang:	Volltext
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