Examining the Size of the Latent Space of Convolutional Variational Autoencoders Trained With Spectral Topographic Maps of EEG Frequency Bands

Examining the Size of the Latent Space of Convolutional Variational Autoencoders Trained With Spectral Topographic Maps of EEG Frequency Bands

Dimensionality reduction and the automatic learning of key features from electroencephalographic (EEG) signals have always been challenging tasks. Variational autoencoders (VAEs) have been used for EEG data generation and augmentation, denoising, and automatic feature extraction. However, investigat...

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Bibliographic Details
Main Authors: Taufique Ahmed, Luca Longo
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Electroencephalography
convolutional variational autoencoder
latent space
deep learning
frequency bands
spectral topographic maps
Online Access:https://ieeexplore.ieee.org/document/9913434/

Internet

https://ieeexplore.ieee.org/document/9913434/

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