The present study aimed to investigate the relationship between motor imagery (MI) assessment (ability and quality) and neurophysiological assessment [transcranial magnetic stimulation (TMS)-induced motor-evoked potentials (MEPs)] during combined MI and action observation (AO; MI + AO). Sixteen subjects completed an MI task playing the piano with both hands, and neurophysiological assessment was performed during the MI task. The Movement Imagery Questionnaire-Revised was adopted to evaluate MI ability, while the visual analogue scale (VAS) was adopted to evaluate MI quality. A TMS pulse was delivered during the MI task, and MEPs were subsequently recorded in the abductor pollicis brevis (APB). We found a significant positive correlation between the VAS score and the TMS-induced MEPs ( = 0.497, < 0.001). These findings suggest that the VAS score could potentially reflect the corticospinal excitability during MI + AO, particularly in complex MI tasks.
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http://dx.doi.org/10.3389/fnhum.2020.581652 | DOI Listing |
Sensors (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.
View Article and Find Full Text PDFBrain Sci
December 2024
Department of Physiotherapy, School of Health Sciences, University of West Attica, 12243 Athens, Greece.
Background/objectives: Dementia is a syndrome which involves cognitive and motor problems such as memory and motor control that impacts the individuals' quality of life. In mental imagery (MI) technique, motor acts are mentally rehearsed without any overt body movements. The aim of this study was to investigate the effectiveness of MI on the quality of life, cognitive, and emotional status of older adults with an early stage of dementia.
View Article and Find Full Text PDFComput Methods Biomech Biomed Engin
January 2025
The School of Computer Science, Hangzhou Dianzi University, Hangzhou, China.
Convolutional neural networks (CNNs) have been widely utilized for decoding motor imagery (MI) from electroencephalogram (EEG) signals. However, extracting discriminative spatial-temporal-spectral features from low signal-to-noise ratio EEG signals remains challenging. This paper proposes MBMSNet , a multi-branch, multi-scale, and multi-view CNN with a lightweight temporal attention mechanism for EEG-Based MI decoding.
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