Background: Repetition of motor imagery improves the motor function of patients with stroke. However, patients who develop severe upper-limb paralysis after chronic stroke often have an impaired ability to induce motor imagery. We have developed a method to passively induce kinesthetic perception using visual stimulation (kinesthetic illusion induced by visual stimulation [KINVIS]).
Objective: This pilot study further investigated the effectiveness of KINVIS in improving the induction of kinesthetic motor imagery in patients with severe upper-limb paralysis after stroke.
Methods: Twenty participants (11 with right hemiplegia and 9 with left hemiplegia; mean time from onset [±standard deviation], 67.0±57.2 months) with severe upper-limb paralysis who could not extend their paretic fingers were included in this study. The ability to induce motor imagery was evaluated using the event-related desynchronization (ERD) recorded during motor imagery before and after the application of KINVIS for 20 min. The alpha- and beta-band ERDs around the premotor, primary sensorimotor, and posterior parietal cortices of the affected and unaffected hemispheres were evaluated during kinesthetic motor imagery of finger extension and before and after the intervention.
Results: Beta-band ERD recorded from the affected hemisphere around the sensorimotor area showed a significant increase after the intervention, while the other ERDs remained unchanged.
Conclusions: In patients with chronic stroke who were unable to extend their paretic fingers for a prolonged period of time, the application of KINVIS, which evokes kinesthetic perception, improved their ability to induce motor imagery. Our findings suggest that although KINVIS is a passive intervention, its short-term application can induce changes related to the motor output system.
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http://dx.doi.org/10.3233/RNN-201030 | DOI Listing |
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).
View Article and Find Full Text PDFJ Cogn
January 2025
Department of Psychology, Western University, London, Canada.
Word norming datasets have become an important resource for psycholinguistic research, and they are based on the underlying assumption that individual differences are inconsequential to the measurement of semantic dimensions. In this pre-registered study we tested this assumption by examining whether individual differences in motor imagery are related to variance in semantic ratings. We collected graspability ratings (i.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Public Health and Exercise Science, Appalachian State University, Boone, NC, USA.
The study aimed to assess the feasibility and potential efficacy of a non-motor intervention utilizing motor imagery (MI) and transcranial direct current stimulation (tDCS) to enhance motor function. The research involved a double-blind, randomized, controlled trial with three groups: MIActive, MISham, and Control. Participants engaged in a cognitively demanding obstacle course, with time and prefrontal activation (ΔO2Hb and ΔHHb) measured across three-time points (Baseline, Post-test, 1-week follow-up).
View Article and Find Full Text PDFSensors (Basel)
December 2024
Instituto de Automática e Informática Industrial, Universitat Politècnica de València, 46022 Valencia, Spain.
In this paper, a bibliometric review is conducted on brain-computer interfaces (BCI) in non-invasive paradigms like motor imagery (MI) and steady-state visually evoked potentials (SSVEP) for applications in rehabilitation and robotics. An exploratory and descriptive approach is used in the analysis. Computational tools such as the biblioshiny application for R-Bibliometrix and VOSViewer are employed to generate data on years, sources, authors, affiliation, country, documents, co-author, co-citation, and co-occurrence.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Electronics and Communication Engineering, Istanbul Technical University, 34467 Istanbul, Istanbul, Turkey.
Classifying Motor Imaging (MI) Electroencephalogram (EEG) signals is of vital importance for Brain-Computer Interface (BCI) systems, but challenges remain. A key challenge is to reduce the number of channels to improve flexibility, portability, and computational efficiency, especially in multi-class scenarios where more channels are needed for accurate classification. This study demonstrates that combining Electrooculogram (EOG) channels with a reduced set of EEG channels is more effective than relying on a large number of EEG channels alone.
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