Bahman Abdi‐Sargezeh

The paper identifies reduced entropy of subthalamic beta bursts as a predictor of freezing of gait in Parkinson's disease.

This study shows that device-embedded accelerometry better tracks Parkinsonian motor states than neural signals during deep brain stimulation.

This study identifies neural states in Parkinson's disease patients using Hidden Markov Models and links them to bradykinesia severity through spectral and temporal features.

The paper analyzes beta burst characteristics and coupling between sensorimotor cortical and subthalamic nucleus (STN) regions in Parkinson's disease patients during naturalistic behaviors.

This paper predicts subthalamic nucleus beta bursts in Parkinson's disease using deep learning, identifying a predictive biomarker for burst onset.

This study develops a deep learning pipeline to predict long and short duration beta bursts from subthalamic nucleus LFP recordings in Parkinson's disease patients, achieving 87% and 85.2% accuracy.