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Found 4 papers, 1 papers with code
State-transition dynamics of resting-state functional magnetic resonance imaging data: Model comparison and test-to-retest analysis
Electroencephalogram (EEG) microstate analysis entails finding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating properties of the estimated state-transition dynamics to observables such as cognition and behavior.
Network Clustering Via Kernel-ARMA Modeling and the Grassmannian The Brain-Network Case
This paper introduces a clustering framework for networks with nodes annotated with time-series data.
Brain-Network Clustering via Kernel-ARMA Modeling and the Grassmannian
Recent advances in neuroscience and in the technology of functional magnetic resonance imaging (fMRI) and electro-encephalography (EEG) have propelled a growing interest in brain-network clustering via time-series analysis.
Riemannian-geometry-based modeling and clustering of network-wide non-stationary time series: The brain-network case
This paper advocates Riemannian multi-manifold modeling in the context of network-wide non-stationary time-series analysis.
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