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...

Full description

Bibliographic Details

Published in:	Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 1 - 5
Main Authors:	Mishra, Aditya, Samin, Ahnaf Mozib, Etemad, Ali, Hashemi, Javad
Format:	Conference Proceeding
Language:	English
Published:	IEEE 06.04.2025
Subjects:	Accuracy Adaptation models Adapters Autoencoders Brain modeling Computational modeling Contrastive learning EEG Electroencephalography GNNs Medical diagnosis Representation learning Speech processing
ISSN:	2379-190X
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!