Helmut Laufs

This paper examines how temporal smoothing and interpolation affect EEG microstate parameters, showing that strong smoothing alters brain dynamics and recommends avoiding intense smoothing to preserve accurate representations.

The paper compares entropy rate and sample entropy metrics for analyzing higher-order EEG microstate syntax in Alzheimer's disease and control groups, finding differences that disappear when normalized to first-order syntax.

The paper evaluates surrogate data generation methods for EEG microstate syntax analysis, finding that Markov chain surrogates are valid while shuffling is not.

The paper maps brain states of reduced consciousness into a low-dimensional space using whole-brain modeling and deep learning, revealing an orderly trajectory from wakefulness to brain injury.

This study identifies canonical brain dynamics by optimizing computational models to match empirical data from functional MRI, revealing stable spiral attractors and bifurcation-related dynamics.

This paper introduces a framework using turbulent dynamics to differentiate brain states by analyzing neuroimaging data with model-free and model-based measures.

The paper develops a framework to distinguish brain states by their stability and reversibility, using whole-brain models to analyze consciousness levels and their response to perturbations.

This study identifies a low-dimensional intrinsic manifold underlying human brain dynamics across wakefulness and sleep, achieving 96% accuracy in decoding brain states.

The paper compares noise-driven multistability and deterministic chaos models to understand brain dynamics, finding chaotic models better reproduce multiple observables across parameter ranges.

This study analyzes connectivity dynamics in fMRI data across wakefulness and sleep stages using ICA and sliding window techniques, showing improved classification of sleep states and motion effects with shorter windows.

This paper introduces a mathematical framework to distinguish human brain states of wakefulness and deep sleep by analyzing the time variability of informational structures derived from a whole-brain dynamical model.

This paper identifies BOLD oscillations in fMRI that differentiate sleep from wake and correlate with spindles and slow waves.