Philippa J. Karoly

This paper presents a simulation framework to evaluate EEG source localisation methods by generating biologically plausible seizure dynamics and EEG signals with known ground truth.

This paper uses structural eigenmodes of the brain to improve EEG source localization in epilepsy, finding geometric eigenmodes slightly outperform connectome eigenmodes.

This study identifies infradian heart rate rhythms in healthy adults using wearable data, finding common multiday patterns and links to environmental and hormonal factors.

The paper uses MEG data to infer neural connectivity and dynamics during altered states of consciousness, finding correlations between consciousness levels and specific brain regions and dynamics.

This paper introduces a whole-brain imaging framework to identify neurophysiological mechanisms, applied to resting-state alpha rhythm, revealing key brain regions and their roles in modulating alpha power.

This study analyzes long-term intracranial EEG recordings to examine variability and circadian patterns of high-frequency activity and epileptiform spikes in patients with drug-resistant epilepsy.

This paper introduces a taxonomy of seizure dynamotypes based on bifurcation theory and applies it to EEG data, identifying 16 dynamotypes and their variations across patients.

This paper presents a deep learning algorithm for computer-assisted EEG review in idiopathic generalized epilepsy, achieving high sensitivity and reducing human review time by 80%-99%.

This study identifies critical slowing as a biomarker for seizure susceptibility using long-term iEEG recordings from epilepsy patients.