Motor imagery facilitates force field learning.

Brain Res

Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan.

Published: June 2011

Humans have the ability to produce an internal reproduction of a specific motor action without any overt motor output. Recent findings show that the processes underlying motor imagery are similar to those active during motor execution and both share common neural substrates. This suggests that the imagery of motor movements might play an important role in acquiring new motor skills. In this study we used haptic robot in conjunction with motor imagery technique to improve learning in a robot-based adaptation task. Two groups of subjects performed reaching movements with or without motor imagery in a velocity-dependent and position-dependent mixed force field. The groups performed movements with motor imagery produced higher after effects and decreased muscle co-contraction with respect to no-motor imagery group. These results showed a positive influence of motor imagery on acquiring new motor skill and suggest that motor learning can be facilitated by mental practice and could be used to increase the rate of adaptation.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.brainres.2011.04.030DOI Listing

Publication Analysis

Top Keywords

motor imagery
24
motor
13
force field
8
acquiring motor
8
movements motor
8
imagery
7
imagery facilitates
4
facilitates force
4
field learning
4
learning humans
4

Similar Publications

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.

View Article and Find Full Text PDF

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 PDF

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 PDF

A cross-domain-based channel selection method for motor imagery.

Med 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 PDF

A Lightweight Network with Domain Adaptation for Motor Imagery Recognition.

Entropy (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.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!