Modeling the acceptability of BCIs for motor rehabilitation after stroke: A large scale study on the general public DOI Creative Commons
Elise Grevet,

Killyam Forge,

Sébastien Tadiello

et al.

Frontiers in Neuroergonomics, Journal Year: 2023, Volume and Issue: 3

Published: Feb. 1, 2023

Introduction Strokes leave around 40% of survivors dependent in their activities daily living, notably due to severe motor disabilities. Brain-computer interfaces (BCIs) have been shown be efficiency for improving recovery after stroke, but this is still far from the level required achieve clinical breakthrough expected by both clinicians and patients. While technical levers improvement identified (e.g., sensors signal processing), fully optimized BCIs are pointless if patients cannot or do not want use them. We hypothesize that BCI acceptability will reduce patients' anxiety levels, while increasing motivation engagement procedure, thereby favoring learning, ultimately, recovery. In other terms, could used as a lever improve efficiency. Yet, studies on based acceptability/acceptance literature missing. Thus, our goal was model context rehabilitation identify its determinants. Methods The main outcomes paper following: i) we designed first ii) created questionnaire assess distributed it sample representative general public France ( N = 753, high response rate strengthens reliability results), iii) validated structure iv) quantified impact different factors population. Results show associated with levels stroke intention them mainly driven perceived usefulness system. addition, providing people clear information regarding functioning scientific relevance had positive influence behavioral . Discussion With propose basis (model) methodology adapted future order study compare results obtained with: stakeholders, i.e., caregivers; populations cultures world; targets, non-clinical applications.

Language: Английский

A Bibliometric Analysis of the Application of Brain-Computer Interface in Rehabilitation Medicine Over the Past 20 Years DOI Creative Commons

Jun-Fu Huang,

Lele Huang, Ying Li

et al.

Journal of Multidisciplinary Healthcare, Journal Year: 2025, Volume and Issue: Volume 18, P. 1297 - 1317

Published: March 1, 2025

This study aims to conduct a bibliometric analysis of the application brain- computer interface (BCI) in rehabilitation medicine, assessing current state, developmental trends, and future potential this field. By systematically analyzing relevant literature, we seek identify key research themes enhance understanding BCI technology rehabilitation. We utilized tools such as VOSviewer CiteSpace screen analyze 426 articles from Web Science Core Collection (WoSCC) database. quantitatively evaluated citation patterns, publication collaboration networks institutions authors uncover hotspots frontier dynamics The findings indicate continuous increase publications since 2003, with notable peak occurring between 2019 2021. revealed that motor imagery, recovery, signal processing are predominant themes. Furthermore, United States China leading volume related medicine. Key include University Tübingen New York State Department Health, significant contributions scholars like Niels Birbaumer. Although on medicine shows efficacy, further exploration certain directions is needed, along promotion interdisciplinary comprehensively address complex real-world issues function impairment. Future should focus optimizing training models, enhancing technical feasibility, exploring home applications facilitate broader adoption

Language: Английский

Citations

2
Effects of brain-computer interface based training on post-stroke upper-limb rehabilitation: a meta-analysis DOI Creative Commons
Dan Li, Ruoyu Li,

Yunping Song

et al.

Journal of NeuroEngineering and Rehabilitation, Journal Year: 2025, Volume and Issue: 22(1)

Published: March 3, 2025

Language: Английский

Citations

1
Brain–Computer Interface-Controlled Exoskeletons in Clinical Neurorehabilitation: Ready or Not? DOI Creative Commons

Annalisa Colucci,

Mareike Vermehren, Alessia Cavallo

et al.

Neurorehabilitation and neural repair, Journal Year: 2022, Volume and Issue: 36(12), P. 747 - 756

Published: Nov. 25, 2022

The development of brain–computer interface-controlled exoskeletons promises new treatment strategies for neurorehabilitation after stroke or spinal cord injury. By converting brain/neural activity into control signals wearable actuators, (B/NEs) enable the execution movements despite impaired motor function. Beyond use as assistive devices, it was shown that—upon repeated over several weeks—B/NEs can trigger recovery, even in chronic paralysis. Recent lightweight robotic comfortable and portable real-world brain recordings, well reliable have paved way B/NEs to enter clinical care. Although are now technically ready broader use, their promotion will critically depend on early adopters, example, research-oriented physiotherapists clinicians who open innovation. Data collected by adopters further elucidate underlying mechanisms B/NE-triggered recovery play a key role increasing efficacy personalized strategies. Moreover, provide indispensable feedback manufacturers necessary improve robustness, applicability, adoption existing therapy plans.

Language: Английский

Citations

38
Poststroke motor, cognitive and speech rehabilitation with brain–computer interface: a perspective review DOI Creative Commons
Ravikiran Mane, Zhenhua Wu, David Wang

et al.

Stroke and Vascular Neurology, Journal Year: 2022, Volume and Issue: 7(6), P. 541 - 549

Published: July 19, 2022

Brain-computer interface (BCI) technology translates brain activity into meaningful commands to establish a direct connection between the and external world. Neuroscientific research in past two decades has indicated tremendous potential of BCI systems for rehabilitation patients suffering from poststroke impairments. By promoting neuronal recovery damaged networks, have achieved promising results motor, cognitive, language Also, several assistive that provide alternative means communication control severely paralysed been proposed enhance patients' quality life. In this article, we present perspective review recent advances challenges used

Language: Английский

Citations

31
Commercial device-based hand rehabilitation systems for stroke patients: State of the art and future prospects DOI Creative Commons
Bo Sheng, Jianyu Zhao, Yanxin Zhang

et al.

Heliyon, Journal Year: 2023, Volume and Issue: 9(3), P. e13588 - e13588

Published: Feb. 10, 2023

Various hand rehabilitation systems have recently been developed for stroke patients, particularly commercial devices. Articles from 10 electronic databases 2010 to 2022 were extracted conduct a systematic review explore the existing training (hardware and software) evaluate their clinical effectiveness. This divided equipment into contact non-contact types. Game-based protocols further classified two types: immersion non-immersion. The results of indicated that majority devices included effective in improving function. Users who underwent with these reported improvements appealing as they helped reduce boredom during sessions. However, also identified some common technical drawbacks devices, such vulnerability effects light. Additionally, it was found currently, there is no commercially available game-based protocol specifically targets rehabilitation. Given ongoing COVID-19 pandemic, need develop safer more engaging community home-based suggests revisions or development new scales evaluation consider current scenario, where in-person interactions might be limited.

Language: Английский

Citations

22
Exploring high-density corticomuscular networks after stroke to enable a hybrid Brain-Computer Interface for hand motor rehabilitation DOI Creative Commons
Floriana Pichiorri, Jlenia Toppi, Valeria de Seta

et al.

Journal of NeuroEngineering and Rehabilitation, Journal Year: 2023, Volume and Issue: 20(1)

Published: Jan. 14, 2023

Brain-Computer Interfaces (BCI) promote upper limb recovery in stroke patients reinforcing motor related brain activity (from electroencephalogaphy, EEG). Hybrid BCIs which include peripheral signals (electromyography, EMG) as control features could be employed to monitor post-stroke abnormalities. To ground the use of corticomuscular coherence (CMC) a hybrid feature for rehabilitative BCI, we analyzed high-density CMC networks (derived from multiple EEG and EMG channels) their relation with deficit by comparing data healthy participants during simple hand tasks.

Language: Английский

Citations

18
Brain-machine interface-based training for improving upper extremity function after stroke: A meta-analysis of randomized controlled trials DOI Creative Commons
Yulei Xie, Yuxuan Yang, Hong Jiang

et al.

Frontiers in Neuroscience, Journal Year: 2022, Volume and Issue: 16

Published: Aug. 3, 2022

Background Upper extremity dysfunction after stroke is an urgent clinical problem that greatly affects patients' daily life and reduces their quality of life. As emerging rehabilitation method, brain-machine interface (BMI)-based training can extract brain signals provide feedback to form a closed-loop rehabilitation, which currently being studied for functional restoration stroke. However, there no reliable medical evidence support the effect BMI-based on upper function This review aimed evaluate efficacy safety improving stroke, as well potential differences in different external devices. Methods English-language literature published before April 1, 2022, was searched five electronic databases using search terms including “brain-computer/machine interface”, “stroke” “upper extremity.” The identified articles were screened, data extracted, methodological included trials assessed. Meta-analysis performed RevMan 5.4.1 software. GRADE method used assess evidence. Results A total 17 studies with 410 post-stroke patients included. showed significantly improved motor [standardized mean difference (SMD) = 0.62; 95% confidence interval (CI) (0.34, 0.90); I 2 38%; p < 0.0001; n 385; random-effects model; moderate-quality evidence]. Subgroup meta-analysis indicated improves both chronic [SMD 0.68; CI (0.32, 1.03), 46%; 0.0002, model] subacute 1.11; 95%CI (0.22, 1.99); 76%; 0.01; compared control interventions, electrical stimulation (FES) (0.67, 1.54); 11%; 0.00001; model]or visual 0.66; (0.2, 1.12); 4%; 0.005; model;] devices BMI more effective than robot. In addition, activities living (ADL) interventions 1.12; (0.65, 1.60); 0%; 80; model]. There statistical dropout rate adverse effects between group group. Conclusion limb ADL patients. combined FES or may be better combination recovery trainings are well-tolerated associated mild effects.

Language: Английский

Citations

23
Electroencephalogram-based adaptive closed-loop brain-computer interface in neurorehabilitation: a review DOI Creative Commons
Wenjie Jin, Xinxin Zhu,

Lifeng Qian

et al.

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

Published: Sept. 20, 2024

Brain-computer interfaces (BCIs) represent a groundbreaking approach to enabling direct communication for individuals with severe motor impairments, circumventing traditional neural and muscular pathways. Among the diverse array of BCI technologies, electroencephalogram (EEG)-based systems are particularly favored due their non-invasive nature, user-friendly operation, cost-effectiveness. Recent advancements have facilitated development adaptive bidirectional closed-loop BCIs, which dynamically adjust users’ brain activity, thereby enhancing responsiveness efficacy in neurorehabilitation. These support real-time modulation continuous feedback, fostering personalized therapeutic interventions that align behavioral responses. By incorporating machine learning algorithms, these BCIs optimize user interaction promote recovery outcomes through mechanisms activity-dependent neuroplasticity. This paper reviews current landscape EEG-based examining applications sensory functions, as well challenges encountered practical implementation. The findings underscore potential technologies significantly enhance patients’ quality life social interaction, while also identifying critical areas future research aimed at improving system adaptability performance. As artificial intelligence continue, evolution sophisticated holds promise transforming neurorehabilitation expanding across various domains.

Language: Английский

Citations

5
Sensorimotor Rhythm-Brain Computer Interface With Audio-Cue, Motor Observation and Multisensory Feedback for Upper-Limb Stroke Rehabilitation: A Controlled Study DOI Creative Commons
Xin Li, Lu Wang,

Si Miao

et al.

Frontiers in Neuroscience, Journal Year: 2022, Volume and Issue: 16

Published: March 11, 2022

Several studies have shown the positive clinical effect of brain computer interface (BCI) training for stroke rehabilitation. This study investigated efficacy sensorimotor rhythm (SMR)-based BCI with audio-cue, motor observation and multisensory feedback post-stroke Furthermore, we discussed interaction between intensity duration in training. Twenty-four patients severe upper limb (UL) deficits were randomly assigned to two groups: 2-week SMR-BCI combined conventional treatment (BCI Group, BG, n = 12) without intervention (Control CG, 12). Motor function was measured using measurement scales, including Fugl-Meyer Assessment-Upper Extremities (FMA-UE; primary outcome measure), Wolf Functional Test (WMFT), Modified Barthel Index (MBI), at baseline (Week 0), post-intervention 2), follow-up week 4). EEG data from allocated BG recorded Week 0 2 quantified by mu suppression means event-related desynchronization (ERD) (8-12 Hz). All functional assessment scores (FMA-UE, WMFT, MBI) significantly improved both groups (p < 0.05). The had higher FMA-UE WMFT improvement 4 compared CG. bilateral hemisphere a trend 2. proposes new effective system demonstrates that feedback, together therapy may promote long-lasting UL improvement. Clinical Trial Registration: [http://www.chictr.org.cn], identifier [ChiCTR2000041119].

Language: Английский

Citations

21
Community mobility and participation assessment of manual wheelchair users: a review of current techniques and challenges DOI Creative Commons

Grace Fasipe,

Maja Goršič, Mohammad Habibur Rahman

et al.

Frontiers in Human Neuroscience, Journal Year: 2024, Volume and Issue: 17

Published: Jan. 5, 2024

According to the World Health Organization, hundreds of individuals commence wheelchair use daily, often due an injury such as spinal cord or through a condition stroke. However, manual users typically experience reductions in individual community mobility and participation. In this review, articles from 2017 2023 were reviewed identify means measuring participation users, factors that can impact these aspects, current rehabilitation techniques for improving them. The selected document best practices utilizing self-surveys, in-clinic assessments, remote tracking GPS accelerometer data, which specialists apply track their patients’ accurately. Furthermore, methods training programs, brain-computer interface triggered functional electric stimulation therapy, community-based programs show potential improve users. Recommendations made highlight avenues future research.

Language: Английский

Citations

4