Modeling Uncertainties in EEG Microstates: Analysis of Real and Imagined Motor Movements Using Probabilistic Clustering-Driven Training of Probabilistic Neural Networks

Modeling Uncertainties in EEG Microstates: Analysis of Real and Imagined Motor Movements Using Probabilistic Clustering-Driven Training of Probabilistic Neural Networks

Part of the process of EEG microstate estimation involves clustering EEG channel data at the global field power (GFP) maxima, very commonly using a modified K-means approach. Clustering has also been done deterministically, despite there being uncertainties in multiple stages of the microstate analy...

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Bibliographic Details
Main Authors: Martin Dinov, Robert Leech
Format: Article
Language:English
Published: Frontiers Media S.A. 2017-11-01
Series:Frontiers in Human Neuroscience
Subjects:
EEG
probabilistic microstates
K-Means
Fuzzy C-Means
multi-layer perceptrons
motor imagery
Online Access:http://journal.frontiersin.org/article/10.3389/fnhum.2017.00534/full

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