Esther Monge-Pereira1, Jaime Ibañez-Pereda2, Isabel M Alguacil-Diego3, Jose I Serrano4, María P Spottorno-Rubio5, Francisco Molina-Rueda6. 1. Motion Analysis, Biomechanics, Ergonomy and Motor Control Laboratory (LAMBECOM group), Physical Therapy, Occupational Therapy, Rehabilitation and Physical Medicine Department, Health Sciences Faculty, Rey Juan Carlos University, Alcorcón, Madrid, Spain; Departamento de Fisioterapia, Terapia Ocupacional, Rehabilitación y Medicina Física, Universidad Rey Juan Carlos, Alcorcón (Madrid), Avda. de Atenas, s/n. CP, 28922, Spain(∗). Electronic address: esther.monge@urjc.es. 2. Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, London, United Kingdom(†). 3. Motion Analysis, Biomechanics, Ergonomy and Motor Control Laboratory (LAMBECOM group), Physical Therapy, Occupational Therapy, Rehabilitation and Physical Medicine Department, Health Sciences Faculty, Rey Juan Carlos University, Alcorcón, Madrid, Spain(‡). 4. Neural and Cognitive Engineering Group, Centro de Automática y Robótica, (CSIC), Arganda del Rey, Spain(§). 5. Acute Stroke Rehabilitation Unit, "La Princesa" Hospital, Madrid, Spain(‖). 6. Motion Analysis, Biomechanics, Ergonomy and Motor Control Laboratory (LAMBECOM group), Physical Therapy, Occupational Therapy, Rehabilitation and Physical Medicine Department, Health Sciences Faculty, Rey Juan Carlos University, Alcorcón, Madrid, Spain(¶).
Abstract
BACKGROUND: Brain-computer interface (BCI) systems have been suggested as a promising tool for neurorehabilitation. However, to date, there is a lack of homogeneous findings. Furthermore, no systematic reviews have analyzed the degree of validation of these interventions for upper limb (UL) motor rehabilitation poststroke. OBJECTIVES: The study aims were to compile all available studies that assess an UL intervention based on an electroencephalography (EEG) BCI system in stroke; to analyze the methodological quality of the studies retrieved; and to determine the effects of these interventions on the improvement of motor abilities. TYPE: This was a systematic review. LITERATURE SURVEY: Searches were conducted in PubMed, PEDro, Embase, Cumulative Index to Nursing and Allied Health, Web of Science, and Cochrane Central Register of Controlled Trial from inception to September 30, 2015. METHODOLOGY: This systematic review compiles all available studies that assess UL intervention based on an EEG-BCI system in patients with stroke, analyzing their methodological quality using the Critical Review Form for Quantitative Studies, and determining the grade of recommendation of these interventions for improving motor abilities as established by the Oxford Centre for Evidence-based Medicine. The articles were selected according to the following criteria: studies evaluating an EEG-based BCI intervention; studies including patients with a stroke and hemiplegia, regardless of lesion origin or temporal evolution; interventions using an EEG-based BCI to restore functional abilities of the affected UL, regardless of the interface used or its combination with other therapies; and studies using validated tools to evaluate motor function. SYNTHESIS: After the literature search, 13 articles were included in this review: 4 studies were randomized controlled trials; 1 study was a controlled study; 4 studies were case series studies; and 4 studies were case reports. The methodological quality of the included papers ranged from 6 to 15, and the level of evidence varied from 1b to 5. The articles included in this review involved a total of 141 stroke patients. CONCLUSIONS: This systematic review suggests that BCI interventions may be a promising rehabilitation approach in subjects with stroke. LEVEL OF EVIDENCE: II.
BACKGROUND: Brain-computer interface (BCI) systems have been suggested as a promising tool for neurorehabilitation. However, to date, there is a lack of homogeneous findings. Furthermore, no systematic reviews have analyzed the degree of validation of these interventions for upper limb (UL) motor rehabilitation poststroke. OBJECTIVES: The study aims were to compile all available studies that assess an UL intervention based on an electroencephalography (EEG) BCI system in stroke; to analyze the methodological quality of the studies retrieved; and to determine the effects of these interventions on the improvement of motor abilities. TYPE: This was a systematic review. LITERATURE SURVEY: Searches were conducted in PubMed, PEDro, Embase, Cumulative Index to Nursing and Allied Health, Web of Science, and Cochrane Central Register of Controlled Trial from inception to September 30, 2015. METHODOLOGY: This systematic review compiles all available studies that assess UL intervention based on an EEG-BCI system in patients with stroke, analyzing their methodological quality using the Critical Review Form for Quantitative Studies, and determining the grade of recommendation of these interventions for improving motor abilities as established by the Oxford Centre for Evidence-based Medicine. The articles were selected according to the following criteria: studies evaluating an EEG-based BCI intervention; studies including patients with a stroke and hemiplegia, regardless of lesion origin or temporal evolution; interventions using an EEG-based BCI to restore functional abilities of the affected UL, regardless of the interface used or its combination with other therapies; and studies using validated tools to evaluate motor function. SYNTHESIS: After the literature search, 13 articles were included in this review: 4 studies were randomized controlled trials; 1 study was a controlled study; 4 studies were case series studies; and 4 studies were case reports. The methodological quality of the included papers ranged from 6 to 15, and the level of evidence varied from 1b to 5. The articles included in this review involved a total of 141 stroke patients. CONCLUSIONS: This systematic review suggests that BCI interventions may be a promising rehabilitation approach in subjects with stroke. LEVEL OF EVIDENCE: II.
Authors: Luca Tonin; Marco Pitteri; Robert Leeb; Huaijian Zhang; Emanuele Menegatti; Francesco Piccione; José Del R Millán Journal: Front Hum Neurosci Date: 2017-06-28 Impact factor: 3.169
Authors: Marianna Semprini; Matteo Laffranchi; Vittorio Sanguineti; Laura Avanzino; Roberto De Icco; Lorenzo De Michieli; Michela Chiappalone Journal: Front Neurol Date: 2018-04-09 Impact factor: 4.003
Authors: Carolina B Tabernig; Camila A Lopez; Lucía C Carrere; Erika G Spaich; Carlos H Ballario Journal: J Rehabil Assist Technol Eng Date: 2018-09-28
Authors: Eduardo Quiles; Ferran Suay; Gemma Candela; Nayibe Chio; Manuel Jiménez; Leandro Álvarez-Kurogi Journal: Int J Environ Res Public Health Date: 2020-01-21 Impact factor: 3.390