Interpreting Disentangled Representations of Person-Specific Convolutional Variational Autoencoders of Spatially Preserving EEG Topographic Maps via Clustering and Visual Plausibility

Interpreting Disentangled Representations of Person-Specific Convolutional Variational Autoencoders of Spatially Preserving EEG Topographic Maps via Clustering and Visual Plausibility

Dimensionality reduction and producing simple representations of electroencephalography (EEG) signals are challenging problems. Variational autoencoders (VAEs) have been employed for EEG data creation, augmentation, and automatic feature extraction. In most of the studies, VAE latent space interpret...

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Bibliographic Details
Main Authors: Taufique Ahmed, Luca Longo
Format: Article
Language:English
Published: MDPI AG 2023-09-01
Series:Information
Subjects:
electroencephalography
convolutional variational autoencoder
latent space interpretation
deep learning
spectral topographic maps
Online Access:https://www.mdpi.com/2078-2489/14/9/489

Internet

https://www.mdpi.com/2078-2489/14/9/489

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