Motor imagery is increasingly being used in clinical settings, such as in neurorehabilitation and brain computer interface (BCI). In stroke, patients lose upper limb function and must re-learn bimanual coordination skills necessary for the activities of daily living. Physiotherapists integrate motor imagery with physical rehabilitation to accelerate recovery. In BCIs, users are often asked to imagine a movement, often with sparse instructions. The EEG pattern that coincides with this cognitive task is captured, then used to execute an external command, such as operating a neuroprosthetic device. As such, BCIs are dependent on the efficient and reliable interpretation of motor imagery. While motor imagery improves patient outcome and informs BCI research, the cognitive and neurophysiological mechanisms which underlie it are not clear. Certain types of motor imagery techniques are more effective than others. For instance, focusing on kinesthetic cues and adopting a first-person perspective are more effective than focusing on visual cues and adopting a third-person perspective. As motor imagery becomes more dominant in neurorehabilitation and BCIs, it is important to elucidate what makes these techniques effective. The purpose of this review is to examine the research to date that focuses on both motor imagery and bimanual coordination. An assessment of current research on these two themes may serve as a useful platform for scientists and clinicians seeking to use motor imagery to help improve bimanual coordination, either through augmenting physical therapy or developing more effective BCIs.
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http://dx.doi.org/10.3389/fnhum.2022.1037410 | 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.
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