Motor imagery-based brain–computer interface rehabilitation programs enhance upper extremity performance and cortical activation in stroke patients

Journal of NeuroEngineering and Rehabilitation, Journal Year: 2024, Volume and Issue: 21(1)

Published: May 29, 2024

Abstract Background The most challenging aspect of rehabilitation is the repurposing residual functional plasticity in stroke patients. To achieve this, numerous plasticity-based clinical programs have been developed. This study aimed to investigate effects motor imagery (MI)-based brain–computer interface (BCI) on upper extremity hand function patients with chronic hemiplegia. Design A 2010 Consolidated Standards for Test Reports (CONSORT)-compliant randomized controlled trial. Methods Forty-six eligible limb dysfunction participated study, six whom dropped out. were randomly divided into a BCI group and control group. received therapy conventional therapy, while only. Fugl–Meyer Assessment Upper Extremity (FMA-UE) score was used as primary outcome evaluate function. Additionally, magnetic resonance imaging (fMRI) scans performed all before after treatment, both resting task states. We measured amplitude low-frequency fluctuation (ALFF), regional homogeneity (ReHo), z conversion ALFF (zALFF), ReHo (ReHo) state. state four tasks: left-hand grasping, right-hand imagining grasping. Finally, meaningful differences assessed using correlation analysis assessments measures. Results total 40 completed 20 Task-related blood-oxygen-level-dependent (BOLD) showed that when performing grasping affected hand, exhibited significant activation ipsilateral middle cingulate gyrus, precuneus, inferior parietal postcentral frontal superior temporal contralateral gyrus. When greater gyrus (medial) treatment. However, decreased relative Resting-state fMRI revealed increased zALFF multiple cerebral regions, including precentral calcarine occipital cuneus, Increased zReHo cuneus pole, observed post-intervention. According subsequent analysis, increase FMA-UE positive mean (r = 0.425, P < 0.05), right 0.399, 0.05). Conclusion In conclusion, effective safe arm severe poststroke hemiparesis. improvements suggested these values can be prognostic measures BCI-based rehabilitation. found related visual spatial processing, suggesting potential avenues refining treatment strategies Trial registration : trial registered Chinese Clinical Registry (number ChiCTR2000034848, July 21, 2020).

Language: Английский

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Rehabilitation with brain-computer interface and upper limb motor function in ischemic stroke: A randomized controlled trial

Med, Journal Year: 2024, Volume and Issue: 5(6), P. 559 - 569.e4

Published: April 19, 2024

Language: Английский

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NeuroFlex: Feasibility of EEG-Based Motor Imagery Control of a Soft Glove for Hand Rehabilitation

Sensors, Journal Year: 2025, Volume and Issue: 25(3), P. 610 - 610

Published: Jan. 21, 2025

Motor impairments resulting from neurological disorders, such as strokes or spinal cord injuries, often impair hand and finger mobility, restricting a person’s ability to grasp perform fine motor tasks. Brain plasticity refers the inherent capability of central nervous system functionally structurally reorganize itself in response stimulation, which underpins rehabilitation brain injuries strokes. Linking voluntary cortical activity with corresponding execution has been identified effective promoting adaptive plasticity. This study introduces NeuroFlex, motion-intent-controlled soft robotic glove for rehabilitation. NeuroFlex utilizes transformer-based deep learning (DL) architecture decode motion intent imagery (MI) EEG data translate it into control inputs assistive glove. The glove’s soft, lightweight, flexible design enables users exercises involving fist formation grasping movements, aligning natural functions practices. results show that accuracy decoding fingers making MI can reach up 85.3%, an average AUC 0.88. demonstrates feasibility detecting assisting patient’s attempted movements using pure thinking through non-intrusive brain–computer interface (BCI). EEG-based aims enhance effectiveness user experience protocols, providing possibility extending therapeutic opportunities outside clinical settings.

Language: Английский

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A Modified Scoping Review of Interventions for Global Post Stroke Spasticity

Toxicon, Journal Year: 2025, Volume and Issue: unknown, P. 108311 - 108311

Published: March 1, 2025

Language: Английский

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Brain–computer interfaces in 2023–2024

Brain‐X, Journal Year: 2025, Volume and Issue: 3(1)

Published: March 1, 2025

Abstract Brain–computer interfaces (BCIs) have advanced at a rapid pace in recent years, particularly the medical domain. This review provides comprehensive summary of progress made BCIs during 2023–2024 period, covering wide range topics from invasive to non‐invasive techniques, and fundamental mechanisms clinical applications. The period saw numerous research breakthroughs applications BCI technology. As hardware software continue evolve, as understanding basic principles deepens, expectation is that innovative inventions will increasingly be introduced practice. Both technologies are paving way for broader It anticipated offer greater hope disease treatment, provide additional methods enhancing human bodily functions, ultimately improve quality life.

Language: Английский

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Lower Limb Muscle Activity during Neural Interface Control: A Neural Interface Based on Motor Imagery of Walking

Neuroscience and Behavioral Physiology, Journal Year: 2025, Volume and Issue: unknown

Published: April 14, 2025

Language: Английский

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Utilizing VR-BCI for motor function rehabilitation

Technology and Disability, Journal Year: 2025, Volume and Issue: unknown

Published: May 16, 2025

Brain-computer interface (BCI) is a promising interactive technology for restoring motor function in patients with neuron diseases. Combining virtual reality (VR) BCI has shown better rehabilitation results, yet research and practical applications remain limited, especially Vietnam. This aims to introduce comprehensive VR-BCI system aid impairments the recovering process. approach seeks improve neuroplasticity recovery through immersive interactions, also advancing development of effective tools, particularly developing countries limited access advanced medical technologies, such as Using affordable Electroencephalogram (EEG) VR devices, optimized Vietnamese healthcare standards, providing an accessible solution home-based therapy. Key features include EEG data processing digital signal techniques, custom-designed scenarios, personalized exercise modules. Initial trials five stroke show improvements control, 2 minimal interference high engagement. Feedback from users professionals highlights system’s usability potential outcomes.

Language: Английский

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Upper extremity training followed by lower extremity training with a brain-computer interface rehabilitation system

Frontiers in Neuroscience, Journal Year: 2024, Volume and Issue: 18

Published: March 4, 2024

Introduction Brain-computer interfaces (BCIs) based on functional electrical stimulation have been used for upper extremity motor rehabilitation after stroke. However, little is known about their efficacy multiple BCI treatments. In this study, 19 stroke patients participated in 25 followed by lower training sessions. Methods Patients’ state was assessed using two sets of clinical scales the The Upper Extremity Fugl-Meyer Assessment (FMA-UE) and 10-Meter Walk Test (10MWT) were primary outcome measures treatments, respectively. Results function as FMA-UE improved an average 4.2 points ( p &lt; 0.001) following treatment. addition, improvements activities daily living clinically relevant hand finger spasticity observed. Patients showed further treatment, with walking speed measured 10MWT increasing 0.15 m/s = 0.001), reflecting a substantial meaningful change. Furthermore, improvement ankle balance mobility Discussion results current study provide evidence that both well combination, are effective facilitating most importantly did not stop first

Language: Английский

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Decoding lower-limb kinematic parameters during pedaling tasks using deep learning approaches and EEG

Medical & Biological Engineering & Computing, Journal Year: 2024, Volume and Issue: 62(12), P. 3763 - 3779

Published: July 19, 2024

Language: Английский

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EEG Amplitude Modulation Analysis across Mental Tasks: Towards Improved Active BCIs

Sensors, Journal Year: 2023, Volume and Issue: 23(23), P. 9352 - 9352

Published: Nov. 23, 2023

Brain-computer interface (BCI) technology has emerged as an influential communication tool with extensive applications across numerous fields, including entertainment, marketing, mental state monitoring, and particularly medical neurorehabilitation. Despite its immense potential, the reliability of BCI systems is challenged by intricacies data collection, environmental factors, noisy interferences, making interpretation high-dimensional electroencephalogram (EEG) a pressing issue. While current trends in research have leant towards improving classification using deep learning-based models, our study proposes use new features based on EEG amplitude modulation (AM) dynamics. Experiments active dataset comprised seven tasks to show importance proposed features, well their complementarity conventional power spectral features. Through combining tasks, 21 binary tests were explored. In 17 these tests, addition significantly improved classifier performance relative density (PSD) only. Specifically, average kappa score for classifications increased from 0.57 0.62 combined feature set. An examination top-selected showed predominance AM-based measures, comprising over 77% top-ranked We conclude this paper in-depth analysis discuss potential neurophysiology.

Language: Английский

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