9 citations · 25 across the 6 of their papers we have counts for
6 papers
Group-level Brain Decoding with Deep Learning
Richard Csaky, Mats Van Es, Oiwi Parker Jones +1
Decoding brain imaging data are gaining popularity, with applications in brain-computer interfaces and the study of neural representations. Decoding is typicallysubject-specific an…
The FMRIB Variational Bayesian Inference Tutorial II: Stochastic Variational Bayes
Michael A. Chappell, Mark W. Woolrich
Bayesian methods have proved powerful in many applications for the inference of model parameters from data. These methods are based on Bayes' theorem, which itself is deceptively s…
Stochastic Variational Bayesian Inference for a Nonlinear Forward Model
Michael A. Chappell, Martin S. Craig, Mark W. Woolrich
Variational Bayes (VB) has been used to facilitate the calculation of the posterior distribution in the context of Bayesian inference of the parameters of nonlinear models from dat…
Resting state brain networks from EEG: Hidden Markov states vs. classical microstates
Tammo Rukat, Adam Baker, Andrew Quinn +1
Functional brain networks exhibit dynamics on the sub-second temporal scale and are often assumed to embody the physiological substrate of cognitive processes. Here we analyse the…
Distinct criticality of phase and amplitude dynamics in the resting brain
Robert Ton, Gustavo Deco, Morten L Kringelbach +2
Converging research suggests that the resting brain operates at the cusp of dynamic instability signified by scale-free temporal correlations. We asked if the scaling properties of…
Dimensionality reduction for time series data
Diego Vidaurre, Iead Rezek, Samuel L. Harrison +2
Despite the fact that they do not consider the temporal nature of data, classic dimensionality reduction techniques, such as PCA, are widely applied to time series data. In this pa…