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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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/14/9/489 |