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A scoping review on motor imagery-based rehabilitation: Potential working mechanisms and clinical application for cognitive function and depression.
Clin Rehabil
January 2025
School of Nursing, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
Objective: To map evidence on the characteristics, effectiveness, and potential mechanisms of motor imagery interventions targeting cognitive function and depression in adults with neurological disorders and/or mobility impairments.
Data Sources: Six English databases (The Cochrane Library, PubMed, Embase, Scopus, Web of Sciences, and PsycINFO), two Chinese databases (CNKI and WanFang), and a gray literature database were searched from inception to December 2024.
Review Methods: This scoping review followed the Joanna Briggs Institute Scoping Review methodology.
Neurophysiological data augmentation for EEG-fNIRS multimodal features based on a denoising diffusion probabilistic model.
Comput Methods Programs Biomed
January 2025
College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, PR China; Shanghai Yangpu Mental Health Center, Shanghai, 200093, PR China. Electronic address:
Background And Objective: The hybrid brain computer interfaces (BCI) combining electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have attracted extensive attention for overcoming the decoding limitations of the single-modality BCI. With the deepening application of deep learning approaches in BCI systems, its significant performance improvement has become apparent. However, the scarcity of brain signal data limits the performance of deep learning models.
View Article and Find Full Text PDFParallel EEG-fNIRS assessments of covert cognition in behaviorally non-responsive ICU patients: A multi-task feasibility study in a case of acute motor sensory axonal neuropathy.
J Neurol
January 2025
Western Institute of Neuroscience, Western University, London, Canada.
Background: Repeat neurological assessment is standard in cases of severe acute brain injury. However, conventional measures rely on overt behavior. Unfortunately, behavioral responses may be difficult or impossible for some patients.
View Article and Find Full Text PDFDecoding imagined movement in people with multiple sclerosis for brain-computer interface translation.
J Neural Eng
January 2025
Department of Biomedical Engineering, The University of Melbourne, Parkville, Melbourne, Victoria, 3010, AUSTRALIA.
Multiple Sclerosis (MS) is a heterogeneous autoimmune-mediated disorder affecting the central nervous system, commonly manifesting as fatigue and progressive limb impairment. This can significantly impact quality of life due to weakness or paralysis in the upper and lower limbs. A Brain-Computer Interface (BCI) aims to restore quality of life through control of an external device, such as a wheelchair.
View Article and Find Full Text PDFPersonalized μ-transcranial alternating current stimulation improves online brain-computer interface control.
J Neural Eng
January 2025
ECE & Neurology, University of Texas at Austin, 301 E. Dean Keeton St. C2100, Austin, Texas, 78712-1139, UNITED STATES.
Objective: A motor imagery (MI)-based brain-computer interface (BCI) enables users to engage with external environments by capturing and decoding electroencephalography (EEG) signals associated with the imagined movement of specific limbs. Despite significant advancements in BCI technologies over the past 40 years, a notable challenge remains: many users lack BCI proficiency, unable to produce sufficiently distinct and reliable MI brain patterns, hence leading to low classification rates in their BCIs. The objective of this study is to enhance the online performance of MI-BCIs in a personalized, biomarker-driven approach using transcranial alternating current stimulation (tACS).
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