On the Dimensionality and Utility of Convolutional Autoencoder’s Latent Space Trained with Topology-Preserving Spectral EEG Head-Maps

On the Dimensionality and Utility of Convolutional Autoencoder’s Latent Space Trained with Topology-Preserving Spectral EEG Head-Maps

Electroencephalography (EEG) signals can be analyzed in the temporal, spatial, or frequency domains. Noise and artifacts during the data acquisition phase contaminate these signals adding difficulties in their analysis. Techniques such as Independent Component Analysis (ICA) require human interventi...

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Bibliographic Details
Main Authors: Arjun Vinayak Chikkankod, Luca Longo
Format: Article
Language:English
Published: MDPI AG 2022-11-01
Series:Machine Learning and Knowledge Extraction
Subjects:
electroencephalography
latent space analysis
sliding windowing
convolutional autoencoders
automatic feature extraction
dense neural network
Online Access:https://www.mdpi.com/2504-4990/4/4/53

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