Objective: Motor imagery may activate the primary motor cortex (M1) and promote functional recovery following stroke. We investigated whether the hemisphere affected by stroke affects performance and M1 activity during motor imagery.
Methods: Twelve stroke patients (6 left, 6 right hemisphere) and eight healthy age-matched adults participated. Experiment 1 assessed the speed and ease of actual and imagined motor performance. Experiment 2 measured corticomotor excitability during imagined movement of each hand separately, and both hands together, using transcranial magnetic stimulation.
Results: For control participants, imagined movements were performed more slowly than actual movements, and right-hand MEPs were facilitated when they imagined moving their right hand or both hands together. Patients reported being able to imagine movements with either hand, despite no measurable facilitation of MEPs in the stroke-affected hand. In left hemisphere patients, MEPs were facilitated in the left hand during imagery of the right hand and both hands together. In right hemisphere patients, motor imagery did not facilitate MEPs in either hand.
Conclusions: Motor imagery does not appear to facilitate the ipsilesional M1 following stroke.
Significance: Motor imagery may play a role in rehabilitating movement planning, but its role in directly facilitating corticomotor output appears limited.
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http://dx.doi.org/10.1016/j.clinph.2007.05.008 | 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.
View Article and Find Full Text PDFCogn Neurodyn
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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