Cargando…

Effect of brain-computer interface training based on non-invasive electroencephalography using motor imagery on functional recovery after stroke - a systematic review and meta-analysis

BACKGROUND: Training with brain-computer interface (BCI) technology in the rehabilitation of patients after a stroke is rapidly developing. Numerous RCT investigated the effects of BCI training (BCIT) on recovery of motor and brain function in patients after stroke. METHODS: A systematic literature...

Descripción completa

Detalles Bibliográficos
Autores principales:	Kruse, Antje, Suica, Zorica, Taeymans, Jan, Schuster-Amft, Corina
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2020
Materias:	Research Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7584076/ https://www.ncbi.nlm.nih.gov/pubmed/33092554 http://dx.doi.org/10.1186/s12883-020-01960-5

Ejemplares similares

Effect of Motor Imagery Training on Motor Learning in Children and Adolescents: A Systematic Review and Meta-Analysis
por: Behrendt, Frank, et al.
Publicado: (2021)

A different point of view: the evaluation of motor imagery perspectives in patients with sensorimotor impairments in a longitudinal study
por: Gäumann, Szabina, et al.
Publicado: (2021)

Motor imagery ability assessments in four disciplines: protocol for a systematic review
por: Suica, Zorica, et al.
Publicado: (2018)

Imagery ability assessments: a cross-disciplinary systematic review and quality evaluation of psychometric properties
por: Suica, Zorica, et al.
Publicado: (2022)

Electroencephalography-Based Brain–Computer Interface Motor Imagery Classification
por: Mohammadi, Ehsan, et al.
Publicado: (2021)

Best practice for motor imagery: a systematic literature review on motor imagery training elements in five different disciplines
por: Schuster, Corina, et al.
Publicado: (2011)

Muscle Activation During Grasping With and Without Motor Imagery in Healthy Volunteers and Patients After Stroke or With Parkinson's Disease
por: Kobelt, Manuela, et al.
Publicado: (2018)

Influence of combined action observation and motor imagery of walking on lower limb reflex modulation in patients after stroke–preliminary results
por: Behrendt, Frank, et al.
Publicado: (2022)

Effect of a four-week virtual reality-based training versus conventional therapy on upper limb motor function after stroke: A multicenter parallel group randomized trial
por: Schuster-Amft, Corina, et al.
Publicado: (2018)

German version of the Chedoke McMaster arm and hand activity inventory (CAHAI-G): intra-rater reliability and responsiveness
por: Behrendt, Frank, et al.
Publicado: (2020)

Using an interactive virtual environment to integrate a digital Action Research Arm Test, motor imagery and action observation to assess and improve upper limb motor function in patients with neuromuscular impairments: a usability and feasibility study protocol
por: Behrendt, Frank, et al.
Publicado: (2018)

Effects of proprioceptive exercises on pain and function in chronic neck- and low back pain rehabilitation: a systematic literature review
por: McCaskey, Michael A, et al.
Publicado: (2014)

A standardized motor imagery introduction program (MIIP) for neuro-rehabilitation: development and evaluation
por: Wondrusch, C., et al.
Publicado: (2013)

The challenge of measuring physiological parameters during motor imagery engagement in patients after a stroke
por: Gäumann, Szabina, et al.
Publicado: (2023)

A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface
por: Singh, Amardeep, et al.
Publicado: (2021)

EMG Muscle Activation Pattern of Four Lower Extremity Muscles during Stair Climbing, Motor Imagery, and Robot-Assisted Stepping: A Cross-Sectional Study in Healthy Individuals
por: Geiger, Damaris E., et al.
Publicado: (2019)

Recovery of Motor Imagery Ability in Stroke Patients
por: de Vries, Sjoerd, et al.
Publicado: (2011)

Comparison of embedded and added motor imagery training in patients after stroke: results of a randomised controlled pilot trial
por: Schuster, Corina, et al.
Publicado: (2012)

High Classification Accuracy of a Motor Imagery Based Brain-Computer Interface for Stroke Rehabilitation Training
por: Irimia, Danut C., et al.
Publicado: (2018)

Efficacy of motor imagery in post-stroke rehabilitation: a systematic review
por: Zimmermann-Schlatter, Andrea, et al.
Publicado: (2008)

Increased motor cortex excitability during motor imagery in brain-computer interface trained subjects
por: Mokienko, Olesya A., et al.
Publicado: (2013)

Classification of Four-Class Motor Imagery Employing Single-Channel Electroencephalography
por: Ge, Sheng, et al.
Publicado: (2014)

Classification of Motor Imagery Electroencephalography Signals Based on Image Processing Method
por: Chen, Zhongye, et al.
Publicado: (2021)

An Intelligent Motor Imagery Detection System Using Electroencephalography with Adaptive Wavelets
por: Khare, Smith K., et al.
Publicado: (2022)

Motor Imagery Experiences and Use: Asking Patients after Stroke Where, When, What, Why, and How They Use Imagery: A Qualitative Investigation
por: Schuster, Corina, et al.
Publicado: (2012)

Therapists' Perspective on Virtual Reality Training in Patients after Stroke: A Qualitative Study Reporting Focus Group Results from Three Hospitals
por: Schmid, Ludwig, et al.
Publicado: (2016)

Vividness of Visual Imagery and Personality Impact Motor-Imagery Brain Computer Interfaces
por: Leeuwis, Nikki, et al.
Publicado: (2021)

EEG datasets for motor imagery brain–computer interface
por: Cho, Hohyun, et al.
Publicado: (2017)

Efficient Classification of Motor Imagery Electroencephalography Signals Using Deep Learning Methods
por: Majidov, Ikhtiyor, et al.
Publicado: (2019)

Applying a brain-computer interface to support motor imagery practice in people with stroke for upper limb recovery: a feasibility study
por: Prasad, Girijesh, et al.
Publicado: (2010)

Facilitating motor imagery-based brain–computer interface for stroke patients using passive movement
por: Arvaneh, Mahnaz, et al.
Publicado: (2016)

Progressive Training for Motor Imagery Brain-Computer Interfaces Using Gamification and Virtual Reality Embodiment
por: Škola, Filip, et al.
Publicado: (2019)

Neurofeedback Training of Alpha Relative Power Improves the Performance of Motor Imagery Brain-Computer Interface
por: Zhou, Qing, et al.
Publicado: (2022)

Comparison of embedded and added motor imagery training in patients after stroke: study protocol of a randomised controlled pilot trial using a mixed methods approach
por: Schuster, Corina, et al.
Publicado: (2009)

Changes in Electroencephalography Complexity using a Brain Computer Interface-Motor Observation Training in Chronic Stroke Patients: A Fuzzy Approximate Entropy Analysis
por: Sun, Rui, et al.
Publicado: (2017)

Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces
por: Leeuwis, Nikki, et al.
Publicado: (2021)

Fast and Efficient Four‑class Motor Imagery Electroencephalography Signal Analysis Using Common Spatial Pattern–Ridge Regression Algorithm for the Purpose of Brain–Computer Interface
por: Seifzadeh, Sahar, et al.
Publicado: (2017)

Analysis of Prognostic Risk Factors Determining Poor Functional Recovery After Comprehensive Rehabilitation Including Motor-Imagery Brain-Computer Interface Training in Stroke Patients: A Prospective Study
por: Wu, Qiong, et al.
Publicado: (2021)

Corrigendum: Neurofeedback training of alpha relative power improves the performance of motor imagery brain-computer interface
por: Zhou, Qing, et al.
Publicado: (2022)

Improvement of brain–computer interface in motor imagery training through the designing of a dynamic experiment and FBCSP
por: Lin, Chun-Ling, et al.
Publicado: (2023)

Cannot write session to /tmp/vufind_sessions/sess_l0kn5mg629u2ppu55htu81qifc