Oren Shriki

The paper shows that cortical resting-state fMRI dynamics are organized along a gradient of distance to criticality, with consistent patterns across subjects.

The paper shows that recurrent neuronal networks near a phase transition amplify slow fluctuations during free recall, supported by simulations and human EEG data.

This paper introduces a data-driven framework for sleep analysis using PCA, GHMM, and ICA to uncover sleep dynamics from electrophysiological signals, achieving results comparable to manual scoring.

This paper introduces a computational method to generate personalized brain stimulation signals for disorders of consciousness, showing in silico that it can induce healthy-like EEG power spectra.

This study applies neural field theory to EEG data to identify correlations between model parameters and consciousness states, highlighting potential biomarkers and physiological bases.

This study shows alpha oscillations in the awake resting-state modulate neural activity through alternating inhibition and excitation bouts, with long-term waxing and waning dynamics not observed during NREM sleep.

This paper classifies seizure dynamotypes from surface EEG using visual and automated methods, finding bifurcation prevalence aligns with intracranial data and varies by clinical factors.

This paper models tinnitus-like hallucinations in an entropy maximization recurrent neural network by simulating sensory deprivation and showing how it drives the network into a tinnitus-like state.

This paper introduces multiscale criticality measures (MsCr) to assess brain function across disorders by analyzing MEG and EEG data.

The paper analyzes broadband resting-state brain activity using neuronal avalanches to reveal long-range power-law correlations in EEG and MEG recordings.

The paper uses lossy compression models to estimate individual memory capacity from P300 responses in an auditory oddball task.

This study predicts cognitive load during intelligence tests using EEG features and machine learning models, showing improved performance with subject-specific neural networks.