Purpose: To investigate the influence of a single session of locomotor-based motor imagery training on motor learning and physical performance.
Patients And Methods: Thirty independent adults aged >65 years took part in the randomized controlled trial. The study was conducted within an exercise science laboratory. Participants were randomly divided into three groups following baseline locomotor testing: motor imagery training, physical training, and control groups. The motor imagery training group completed 20 imagined repetitions of a locomotor task, the physical training group completed 20 physical repetitions of a locomotor task, and the control group spent 25 minutes playing mentally stimulating games on an iPad. Imagined and physical performance times were measured for each training repetition. Gait speed (preferred and fast), timed-up-and-go, gait variability and the time to complete an obstacle course were completed before and after the single training session.
Results: Motor learning occurred in both the motor imagery training and physical training groups. Motor imagery training led to refinements in motor planning resulting in imagined movements better matching the physically performed movement at the end of training. Motor imagery and physical training also promoted improvements in some locomotion outcomes as demonstrated by medium to large effect size improvements after training for fast gait speed and timed-up-and-go. There were no training effects on gait variability.
Conclusion: A single session of motor imagery training promoted motor learning of locomotion in independent older adults. Motor imagery training of a specific locomotor task also had a positive transfer effect on related physical locomotor performance outcomes.
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http://dx.doi.org/10.2147/CIA.S164401 | DOI Listing |
The complementary strengths of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have driven extensive research into integrating these two noninvasive modalities to better understand the neural mechanisms underlying cognitive, sensory, and motor functions. However, the precise neural patterns associated with motor functions, especially imagined movements, remain unclear. Specifically, the correlations between electrophysiological responses and hemodynamic activations during executed and imagined movements have not been fully elucidated at a whole-brain level.
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December 2025
School of Computer Science, Hangzhou Dianzi University, Hangzhou, 310018 Zhejiang China.
The increasing adoption of wearable technologies highlights the potential of electroencephalogram (EEG) signals for biometric recognition. However, the intrinsic variability in cross-session EEG data presents substantial challenges in maintaining model stability and reliability. Moreover, the diversity within single-task protocols complicates achieving consistent and generalized model performance.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Mechanical Engineering, College of Engineering, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia.
Enhancing motor disability assessment and its imagery classification is a significant concern in contemporary medical practice, necessitating reliable solutions to improve patient outcomes. One promising avenue is the use of brain-computer interfaces (BCIs), which establish a direct communication pathway between users and machines. This technology holds the potential to revolutionize human-machine interaction, especially for individuals diagnosed with motor disabilities.
View Article and Find Full Text PDFMed Biol Eng Comput
January 2025
State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing, University, Chongqing, 400044, People's Republic of China.
Selecting channels for motor imagery (MI)-based brain-computer interface (BCI) systems can not only enhance the portability of the systems, but also improve the decoding performance. Hence, we propose a cross-domain-based channel selection (CDCS) approach, which effectively minimizes the number of EEG channels used while maintaining high accuracy in MI recognition. The EEG source imaging (ESI) technique is employed to map scalp EEG into the cortical source domain.
View Article and Find Full Text PDFEntropy (Basel)
December 2024
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300384, China.
Brain-computer interfaces (BCI) are an effective tool for recognizing motor imagery and have been widely applied in the motor control and assistive operation domains. However, traditional intention-recognition methods face several challenges, such as prolonged training times and limited cross-subject adaptability, which restrict their practical application. This paper proposes an innovative method that combines a lightweight convolutional neural network (CNN) with domain adaptation.
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