Repetitive peripheral magnetic stimulation is a novel non-invasive technique for applying repetitive magnetic stimulation to the peripheral nerves and muscles. Contrarily, a person imagines that he/she is exercising during motor imagery. Resting-state electroencephalography can evaluate the ability of motor imagery; however, the effects of motor imagery and repetitive peripheral magnetic stimulation on resting-state electroencephalography are unknown. We examined the effects of motor imagery and repetitive peripheral magnetic stimulation on the vividness of motor imagery and resting-state electroencephalography. The participants were divided into a motor imagery group and motor imagery and repetitive peripheral magnetic stimulation group. They performed 60 motor imagery tasks involving wrist dorsiflexion movement. In the motor imagery and repetitive peripheral magnetic stimulation group, we applied repetitive peripheral magnetic stimulation to the extensor carpi radialis longus muscle during motor imagery. We measured the vividness of motor imagery and resting-state electroencephalography before and after the task. Both groups displayed a significant increase in the vividness of motor imagery. The motor imagery and repetitive peripheral magnetic stimulation group exhibited increased β activity in the anterior cingulate cortex by source localization for electroencephalography. Hence, combined motor imagery and repetitive peripheral magnetic stimulation changes the resting-state electroencephalography activity and may promote motor imagery.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9688706 | PMC |
http://dx.doi.org/10.3390/brainsci12111548 | DOI Listing |
Brain Behav
January 2025
School and Graduate Institute of Physical Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan.
Background: Different modes of motor acquisition, including motor execution (ME), motor imagery (MI), action observation (AO), and mirror visual feedback (MVF), are often used when learning new motor behavior and in clinical rehabilitation.
Purpose: The aim of this study was to investigate differences in brain activation during different motor acquisition modes among healthy young adults.
Methods: This cross-sectional study recruited 29 healthy young adults.
Front Neurol
December 2024
Department of Physical Therapy, School of Health Sciences, Ariel University, Ariel, Israel.
Children with attention deficit hyperactivity disorder (ADHD) exhibit various degrees of motor and cognitive impairments in fine and gross motor skills. These impairments impact social functioning, while also hindering academic achievement, self-esteem, and participation. Specifically, motor impairments are not fully addressed by current therapies.
View Article and Find Full Text PDFSensors (Basel)
December 2024
College of Computer and Information Sciences (CCIS), King Saud University, Riyadh 11543, Saudi Arabia.
One of the most promising applications for electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is motor rehabilitation through motor imagery (MI) tasks. However, current MI training requires physical attendance, while remote MI training can be applied anywhere, facilitating flexible rehabilitation. Providing remote MI training raises challenges to ensuring an accurate recognition of MI tasks by healthcare providers, in addition to managing computation and communication costs.
View Article and Find Full Text PDFLife (Basel)
December 2024
CESPU, Instituto Politécnico de Saúde do Norte, Escola Superior de Saúde do Vale do Ave, 4760-409 Vila Nova de Famalicão, Portugal.
Arthrogenic muscle inhibition (AMI) following ACL injury or reconstruction is a common issue that affects muscle activation and functional recovery. Thus, the objective of this study was to systematize the literature on the effects of physiotherapy interventions in the rehabilitation of AMI after ACL injury or reconstruction. A systematic review was conducted following the PRISMA guidelines.
View Article and Find Full Text PDFBrain Sci
December 2024
Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 50/a, 1083 Budapest, Hungary.
: Accurately classifying Electroencephalography (EEG) signals is essential for the effective operation of Brain-Computer Interfaces (BCI), which is needed for reliable neurorehabilitation applications. However, many factors in the processing pipeline can influence classification performance. The objective of this study is to assess the effects of different processing steps on classification accuracy in EEG-based BCI systems.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!