Related Experiment Video
Updated: Jan 10, 2026

Controlling Parkinson's Disease With Adaptive Deep Brain Stimulation
Published on: July 16, 2014
Lower limb motor effects of DBS neurofeedback in Parkinson's disease assessed through IMU-based UPDRS movement
Lena Salzmann1, Oliver Bichsel2,3, Manabu Rohr-Fukuma2,3
1Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, 8092, Zurich, Switzerland. relab.publications@hest.ethz.ch.
Abstract:
Parkinson's disease (PD) is characterized by progressive motor impairments, including lower limb dysfunction, leading to reduced mobility and increased fall risk. To counteract these deficits, neurofeedback based on deep brain stimulation (DBS) electrodes has been proposed as a novel approach to mitigate motor symptoms via modulation of abnormal beta-oscillations in the subthalamic nucleus. However, its potential to improve motor symptoms has yet to be fully established. This study examined whether a single session of DBS-based neurofeedback could have a short term effect on movement quality, quantified through inertial measurement unit recordings. Ten PD patients performed two standardized motor tasks, foot stomping and hand pronation-supination, from the Unified Parkinson's Disease Rating Scale. Movement quality metrics from inertial measurement units were extracted and compared before and after neurofeedback-induced beta-power downregulation. Beta-power was successfully reduced by -12.42% on average, and the reduction was associated with significant improvements in lower limb movement quality metrics-acceleration magnitude (p = 0.037), movement speed (steps per second: p = 0.010; mean peak velocity: p = 0.002), and reduced halts (p = 0.020)-with a strong coupling between beta reduction and speed gain (Spearman ρ = 0.976, p < 0.001). No significant improvements were observed in upper limb movements. These findings indicate that neurofeedback-driven downregulation of beta-power produces measurable enhancements in lower limb movement quality, captured through wearable sensor metrics. Future work should assess whether these improvements translate into lasting functional benefits and validate the clinical relevance of these metrics.

