Background: Phantom limb pain (PLP) can be defined as pain in a missing part of the limb. It is reported in 50%-80% of people with amputation.
Objectives: To provide an overview of the effectiveness of graded motor imagery (GMI) and the techniques which form it on PLP in amputees.
Study Design: Systematic review.
Methods: Two authors independently selected relevant studies, screened the articles for methodological validity and risk of bias, and extracted the data. Inclusion criteria used were clinical studies, written in English or Spanish, using GMI, laterality recognition, motor imagery, mirror therapy, or a combination of some of them as an intervention in amputated patients, and one of the outcomes was PLP, and it was assessed using a validated scale. The databases used were PubMed, Scopus, Web of Science, CINAHL, and PEDro.
Results: Fifteen studies were included in the review. After the intervention, all the groups in which the GMI or one of the techniques that comprise it was used showed decrease in PLP.
Conclusion: The 3 GMI techniques showed effectiveness in decreasing PLP in amputees, although it should be noted that the application of the GMI showed better results.
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http://dx.doi.org/10.1097/PXR.0000000000000293 | DOI Listing |
Sensors (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.
View Article and Find Full Text PDFBrain Sci
January 2025
IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148 Milan, Italy.
: Parkinson's disease (PD) is a neurodegenerative disorder, characterised by cardinal motor features and a multitude of non-motor manifestations. Among them, cognitive impairment in PD has been recognised as a defined clinical entity, and it might lead to an increased risk of developing dementia. Consequently, the present review aimed to ascertain the available interventions for the training of cognitive abilities in persons with PD (PwPD).
View Article and Find Full Text PDFBrain Sci
January 2025
School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China.
Background: Decoding motor intentions from electroencephalogram (EEG) signals is a critical component of motor imagery-based brain-computer interface (MI-BCIs). In traditional EEG signal classification, effectively utilizing the valuable information contained within the electroencephalogram is crucial.
Objectives: To further optimize the use of information from various domains, we propose a novel framework based on multi-domain feature rotation transformation and stacking ensemble for classifying MI tasks.
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