A Task-Related EEG Microstate Clustering Algorithm Based on Spatial Patterns, Riemannian Distance, and a Deep Autoencoder

A Task-Related EEG Microstate Clustering Algorithm Based on Spatial Patterns, Riemannian Distance, and a Deep Autoencoder

Background: The segmentation of electroencephalography (EEG) signals into a limited number of microstates is of significant importance in the field of cognitive neuroscience. Currently, the microstate analysis algorithm based on global field power has demonstrated its efficacy in clustering resting-...

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Bibliographic Details
Main Authors: Shihao Pan, Tongyuan Shen, Yongxiang Lian, Li Shi
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Brain Sciences
Subjects:
microstate analysis
EEG clustering
spatial pattern
Riemannian distance
deep autoencoder
Online Access:https://www.mdpi.com/2076-3425/15/1/27
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