Alireza Gharabaghi

Transcutanenous auricular vagus nerve stimulation affects sustained attention in an order-dependent manner.

This study shows that transcutaneous auricular vagus nerve stimulation (taVNS) affects sustained attention differently depending on the order of stimulation, with effects persisting into a subsequent session.

EEG entropy reflects both intrinsic and stimulation-induced corticospinal excitability

This study shows that EEG entropy from short segments reflects both baseline and stimulation-induced changes in cortical excitability, potentially improving TMS intervention strategies.

Real-Time Spatiotemporal Filtering for Artifact-Free EEG during Electrical Neurostimulation

This paper introduces a real-time spatiotemporal filtering method to remove EEG artifacts caused by electrical neurostimulation while preserving brain signal integrity.

EEG denoising during transcutaneous auricular vagus nerve stimulation across simulated, phantom and human data

The paper evaluates denoising techniques for EEG data during transcutaneous auricular vagus nerve stimulation, finding that GED/SASS and SSP are most effective in reducing artifacts while preserving signal integrity.

Sex differences in prenatal development of neural complexity in the human brain

This study examines how neural complexity changes during prenatal development and finds it decreases with maturation, more rapidly in male fetuses.

Brain signal complexity and aperiodicity predict human corticospinal excitability

This study shows that broadband EEG measures like aperiodic exponent and entropy predict corticospinal excitability better than traditional band-limited features.

Phase-specific stimulation reveals consistent sinusoidal modulation of human corticospinal excitability along the oscillatory beta cycle

The paper uses real-time EEG-triggered TMS to show that corticospinal excitability modulates sinusoidally at 24Hz across individuals and sessions.

Phase-specific stimulation of the human brain with real-time measurement instead of prediction

This paper introduces a real-time brain stimulation method that improves phase-specific precision by integrating measurement and stimulation without prediction.

Direct Modulation Index: a measure of phase amplitude coupling for neurophysiology data

This paper introduces a new measure, direct modulation index (dMI), for phase-amplitude coupling in neurophysiology data, showing improved accuracy and robustness compared to existing methods.

Directional Absolute Coherence: a phase-based measure of effective connectivity for neurophysiology data

This paper introduces a new effective connectivity measure called directional absolute coherence (DAC) and demonstrates its reliability in estimating neural communication direction and magnitude.

FiNN: A toolbox for neurophysiological network analysis

FiNN is a Python toolbox for neurophysiological network analysis that offers efficient data processing and visualization tools, outperforming existing frameworks in speed and memory usage.

Risks in signal processing pipelines influencing the estimation of phase dependency for EEG-TMS

The paper investigates how signal processing pipelines affect phase dependency estimation in TMS-EEG studies and recommends best practices to improve reliability.