Related Experiment Video
Updated: Mar 17, 2026

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients
Published on: September 1, 2023
Shutting Down Sensorimotor Interferences after Stroke: A Proof-of-Principle SMR Neurofeedback Study
Johanna L Reichert1, Silvia E Kober1, Daniela Schweiger2
1Institute of Psychology, University of GrazGraz, Austria; BioTechMed GrazGraz, Austria.
Introduction:
Neurofeedback training aims at learning self-regulation of brain activity underlying cognitive, emotional or physiological functions. Despite of promising investigations on neurofeedback as a tool for cognitive rehabilitation in neurological diseases, such as after stroke, there is still a lack of research on feasibility and efficiency of neurofeedback in this field.
Methods:
The present study aimed at investigating behavioral and electrophysiological effects of 10 sessions of sensorimotor rhythm (SMR) neurofeedback in a 74-years-old stroke patient (UG20). Based on previous results in healthy young participants, we hypothesized that SMR neurofeedback leads to a decrease in sensorimotor interferences and improved stimulus processing, reflected by changes in event-related potentials (ERPs) and electrophysiological coherence. To assess whether UG20 benefited from the training as much as healthy persons of a similar age, a healthy control group of N = 10 elderly persons was trained as well. Before and after neurofeedback training, participants took part in a multichannel electroencephalography measurement conducted during a non-verbal and a verbal learning task.
Results:
Both UG20 and the healthy controls were able to regulate their SMR activity during neurofeedback training. Moreover, in a non-verbal learning task, changes in ERPs and coherence were observed after training: UG20 showed a better performance in the non-verbal learning task and a higher P3 amplitude after training than before, and coherence between central and parietal electrodes decreased after training. The control group also showed a behavioral improvement in the non-verbal learning task and tendencies for higher P3 amplitudes and decreased central-parietal coherence after training. Single-case analyses indicated that the changes observed in UG20 were not smaller than the changes in healthy controls.
Conclusion:
Neurofeedback can be successfully applied in a stroke patient and in healthy elderly persons. We suggest that SMR neurofeedback leads to a shutting-down of sensorimotor interferences which benefits semantic encoding and retrieval.
More Related Videos
11:06A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation
Published on: April 12, 2016
08:53Randomized, Triple-Blind, and Parallel-Controlled Trial of Transcranial Direct Current Stimulation for Cognitive Rehabilitation after Stroke
Published on: June 6, 2025