Online citations, reference lists, and bibliographies.

Brain-machine Interface In Chronic Stroke Rehabilitation: A Controlled Study.

Ander Ramos-Murguialday, Doris Broetz, Massimiliano Rea, Leonhard Läer, Ozge Uygungul Yilmaz, Fabricio Lima Brasil, Giulia Liberati, Marco Rocha Curado, Eliana Garcia-Cossio, Alexandros Vyziotis, Woosang Cho, Manuel Agostini, Ernesto Soares, Surjo R. Soekadar, Andrea Caria, Leonardo G. Cohen, Niels Birbaumer
Published 2013 · Medicine
Cite This
Download PDF
Analyze on Scholarcy
OBJECTIVE Chronic stroke patients with severe hand weakness respond poorly to rehabilitation efforts. Here, we evaluated efficacy of daily brain-machine interface (BMI) training to increase the hypothesized beneficial effects of physiotherapy alone in patients with severe paresis in a double-blind sham-controlled design proof of concept study. METHODS Thirty-two chronic stroke patients with severe hand weakness were randomly assigned to 2 matched groups and participated in 17.8 ± 1.4 days of training rewarding desynchronization of ipsilesional oscillatory sensorimotor rhythms with contingent online movements of hand and arm orthoses (experimental group, n = 16). In the control group (sham group, n = 16), movements of the orthoses occurred randomly. Both groups received identical behavioral physiotherapy immediately following BMI training or the control intervention. Upper limb motor function scores, electromyography from arm and hand muscles, placebo-expectancy effects, and functional magnetic resonance imaging (fMRI) blood oxygenation level-dependent activity were assessed before and after intervention. RESULTS A significant group × time interaction in upper limb (combined hand and modified arm) Fugl-Meyer assessment (cFMA) motor scores was found. cFMA scores improved more in the experimental than in the control group, presenting a significant improvement of cFMA scores (3.41 ± 0.563-point difference, p = 0.018) reflecting a clinically meaningful change from no activity to some in paretic muscles. cFMA improvements in the experimental group correlated with changes in fMRI laterality index and with paretic hand electromyography activity. Placebo-expectancy scores were comparable for both groups. INTERPRETATION The addition of BMI training to behaviorally oriented physiotherapy can be used to induce functional improvements in motor function in chronic stroke patients without residual finger movements and may open a new door in stroke neurorehabilitation.

This paper is referenced by
The User-Centered Design as Novel Perspective for Evaluating the Usability of BCI-Controlled Applications
Andrea Kübler (2014)
Context-aware quality of life telemonitoring for a novel healthcare paradigm
Felip Miralles Barrachina (2016)
Chronic Stroke Outcome Measures for Motor Function Intervention Trials: Expert Panel Recommendations.
Cheryl D Bushnell (2015)
Reconstruction of hand, elbow and shoulder actual and imagined trajectories in 3D space using EEG slow cortical potentials.
Ronen Sosnik (2019)
Enhancement of motor-imagery ability via combined action observation and motor-imagery training with proprioceptive neurofeedback
Yumie Ono (2018)
A Survey on the Use of Haptic Feedback for Brain-Computer Interfaces and Neurofeedback
Mathis Fleury (2020)
Functional recovery from chronic writer’s cramp by brain-computer interface rehabilitation: a case report
Yasunari Hashimoto (2014)
Open Access Dataset for EEG+NIRS Single-Trial Classification
Jaeyoung Shin (2017)
Neuroscience-Based Rehabilitation for Stroke Patients
Takayuki Kodama (2017)
Exploring Training Effect in 42 Human Subjects Using a Non-invasive Sensorimotor Rhythm Based Online BCI
Jianjun Meng (2019)
Neurorehabilitation therapy of patients with severe stroke based on functional electrical stimulation commanded by a brain computer interface
Carolina B. Tabernig (2018)
Enhancing Engagement during Robot-Assisted Rehabilitation Integrated with Motor Imagery Task
Tianyu Jia (2019)
Brain-Machine Interface in Chronic Stroke: Randomized Trial Long-Term Follow-up
Ander Ramos-Murguialday (2019)
Exploring representations of human grasping in neural, muscle and kinematic signals
Andreea Ioana Sburlea (2018)
Adaptation in motor imagery brain-computer interfaces and its implication in rehabilitation
Cuntai Guan (2016)
A novel BCI-controlled pneumatic glove system for home-based neurorehabilitation
Aodhan L. Coffey (2014)
Non-invasive Detection of Cortical Control Signals for Brain-Computer Interfaces
Melissa M. Smith (2016)
Effect of neurofeedback training on event-related desynchronization strength by motor imagery
Kenya Wada (2017)
Ubiquitous and Wearable ComputingSolutions for Enhancing MotorRehabilitation of the Upper ExtremityPost-Stroke
Aodhan L. Coffey (2016)
Brain-machine Interface in Robot-assisted Neurorehabilitation for Patients with Stroke and Upper Extremity Weakness – the Therapeutic Turning Point
Jung Hwan Kim (2016)
Neurofeedback in the Rehabilitation of Patients with Motor Disorders after Stroke
M. S. Kovyazina (2019)
Practical electroencephalography (EEG) applications in stroke rehabilitation: Towards brain-computer interface (BCI) setup and motor function assessment
Xingye Zhang (2019)
A clinical trial to study changes in neural activity and motor recovery following brain-machine interface enabled robot-assisted stroke rehabilitation
Nutan A. Bhagat (2020)
Low-Frequency Brain Oscillations Track Motor Recovery in Human Stroke.
Marlene Bönstrup (2019)
Brain networks and their relevance for stroke rehabilitation
Adrian G. Guggisberg (2019)
Magnetoencephalography in Stroke Recovery and Rehabilitation
Andrea Paggiaro (2016)
EEG-Based Mental Task Classification with Convolutional Neural Networks - Parallel vs 2D Data Representation
Bartlomiej Stasiak (2018)
Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns
Camille Jeunet (2015)
Low Latency Estimation of Motor Intentions to Assist Reaching Movements along Multiple Sessions in Chronic Stroke Patients: A Feasibility Study
Jaime Ibáñez (2017)
Analyzing electrode configurations to detect intention of pedaling initiation through EEG signals
Marisol Rodriguez-Ugarte (2016)
Brain–robot interface driven plasticity: Distributed modulation of corticospinal excitability
Dominic Kraus (2016)
Predicting workload profiles of brain-robot interface and electromygraphic neurofeedback with cortical resting-state networks: personal trait or task-specific challenge?
Meike Fels (2015)
See more
Semantic Scholar Logo Some data provided by SemanticScholar