Use of Electroencephalography Brain‐Computer Interface Systems as a Rehabilitative Approach for Upper Limb Function After a Stroke: A Systematic Review

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.

[1]  Akio Kimura,et al.  Brain-computer interface with somatosensory feedback improves functional recovery from severe hemiplegia due to chronic stroke , 2014, Front. Neuroeng..

[2]  E. Steultjens,et al.  Role of Environmental Factors on Resuming Valued Activities Poststroke: A Systematic Review of Qualitative and Quantitative Findings. , 2016, Archives of physical medicine and rehabilitation.

[3]  Reorganization of Motor Execution Rather Than Preparation in Poststroke Hemiparesis , 2005 .

[4]  Carlo Menon,et al.  Assisting drinking with an affordable BCI-controlled wearable robot and electrical stimulation: a preliminary investigation , 2014, Journal of NeuroEngineering and Rehabilitation.

[5]  Gernot R. Müller-Putz,et al.  Non-motor tasks improve adaptive brain-computer interface performance in users with severe motor impairment , 2014, Front. Neurosci..

[6]  C. Neuper,et al.  Relationship Between Electrical Brain Responses to Motor Imagery and Motor Impairment in Stroke , 2012, Stroke.

[7]  C. Y. Shee,et al.  Post-acute stroke patients use brain-computer interface to activate electrical stimulation , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[8]  P. F. M. J. Verschure,et al.  Using a Hybrid Brain Computer Interface and Virtual Reality System to Monitor and Promote Cortical Reorganization through Motor Activity and Motor Imagery Training , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[9]  J. Wolpaw,et al.  Brain–computer interfaces in neurological rehabilitation , 2008, The Lancet Neurology.

[10]  F. L. D. Silva,et al.  Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.

[11]  P. Duncan,et al.  Measurement of Motor Recovery After Stroke: Outcome Assessment and Sample Size Requirements , 1992, Stroke.

[12]  C. Calautti,et al.  Functional Neuroimaging Studies of Motor Recovery After Stroke in Adults: A Review , 2003, Stroke.

[13]  Vivek Prabhakaran,et al.  Characterizing relationships of DTI, fMRI, and motor recovery in stroke rehabilitation utilizing brain-computer interface technology , 2014, Front. Neuroeng..

[14]  S. Silvoni,et al.  Brain-Computer Interface in Stroke: A Review of Progress , 2011, Clinical EEG and neuroscience.

[15]  Domenico Formica,et al.  Modulation of brain plasticity in stroke: a novel model for neurorehabilitation , 2014, Nature Reviews Neurology.

[16]  Jessica Cantillo-Negrete,et al.  An approach to improve the performance of subject-independent BCIs-based on motor imagery allocating subjects by gender , 2014, BioMedical Engineering OnLine.

[17]  T. Ward,et al.  Brain computer interfaces for neurorehabilitation – its current status as a rehabilitation strategy post-stroke. , 2015, Annals of physical and rehabilitation medicine.

[18]  Ethan R. Buch,et al.  Think to Move: a Neuromagnetic Brain-Computer Interface (BCI) System for Chronic Stroke , 2008, Stroke.

[19]  S. Coyle,et al.  Brain–computer interfaces: a review , 2003 .

[20]  J. Peters,et al.  Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery , 2011, Journal of neural engineering.

[21]  Nicola Smania,et al.  Modulation of event-related desynchronization in robot-assisted hand performance: brain oscillatory changes in active, passive and imagined movements , 2013, Journal of NeuroEngineering and Rehabilitation.

[22]  G. Prasad,et al.  Applying a brain-computer interface to support motor imagery practice in people with stroke for upper limb recovery: a feasibility study , 2010, Journal of NeuroEngineering and Rehabilitation.

[23]  L. Cohen,et al.  Brain–machine interface in chronic stroke rehabilitation: A controlled study , 2013, Annals of neurology.

[24]  Moritz Grosse-Wentrup,et al.  Using brain–computer interfaces to induce neural plasticity and restore function , 2011, Journal of neural engineering.

[25]  J. Ushiba,et al.  Event-related desynchronization reflects downregulation of intracortical inhibition in human primary motor cortex. , 2013, Journal of neurophysiology.

[26]  Ning Jiang,et al.  Peripheral Electrical Stimulation Triggered by Self-Paced Detection of Motor Intention Enhances Motor Evoked Potentials , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[27]  A. E. Schulman,et al.  Functional coupling and regional activation of human cortical motor areas during simple, internally paced and externally paced finger movements. , 1998, Brain : a journal of neurology.

[28]  G. Yue,et al.  Prolonged cognitive planning time, elevated cognitive effort, and relationship to coordination and motor control following stroke , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[29]  R. Padmavathi,et al.  A review on EEG based brain computer interface systems feature extraction methods , 2016 .

[30]  Reza Fazel-Rezai,et al.  A Review of Hybrid Brain-Computer Interface Systems , 2013, Adv. Hum. Comput. Interact..

[31]  I. Miyai,et al.  Near-infrared Spectroscopy–mediated Neurofeedback Enhances Efficacy of Motor Imagery–based Training in Poststroke Victims: A Pilot Study , 2013, Stroke.

[32]  M. Pereiraa,et al.  se of virtual reality systems as proprioception method in erebral palsy : clinical practice guideline , 2016 .

[33]  J. Grotta,et al.  Guidelines for the Management of Patients With Acute Ischemic Stroke A Statement for Healthcare Professionals , 2005 .

[34]  J. Zhou,et al.  EEG-based Discrimination of Elbow/Shoulder Torques using Brain Computer Interface Algorithms: Implications for Rehabilitation , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[35]  Wim L. C. Rutten,et al.  Temporal evolution of event-related desynchronization in acute stroke: A pilot study , 2014, Clinical Neurophysiology.

[36]  T. Jones,et al.  Use it and/or lose it—experience effects on brain remodeling across time after stroke , 2014, Front. Hum. Neurosci..

[37]  Febo Cincotti,et al.  EEG-based Brain-Computer Interface to support post-stroke motor rehabilitation of the upper limb , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[38]  Dean J Krusienski,et al.  Brain-computer interfaces in medicine. , 2012, Mayo Clinic proceedings.

[39]  E. M. Pereira,et al.  Empleo de sistemas de realidad virtual como método de propiocepción en parálisis cerebral: guía de práctica clínica , 2014 .

[40]  J L Pons,et al.  Detection of the onset of upper-limb movements based on the combined analysis of changes in the sensorimotor rhythms and slow cortical potentials , 2014, Journal of neural engineering.

[41]  Darren J. Leamy,et al.  An exploration of EEG features during recovery following stroke – implications for BCI-mediated neurorehabilitation therapy , 2014, Journal of NeuroEngineering and Rehabilitation.

[42]  Keum-Shik Hong,et al.  fNIRS-based brain-computer interfaces: a review , 2015, Front. Hum. Neurosci..

[43]  Laura Astolfi,et al.  A new descriptor of neuroelectrical activity during BCI-assisted motor imagery-based training in stroke patients , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[44]  David T. Bundy,et al.  Using ipsilateral motor signals in the unaffected cerebral hemisphere as a signal platform for brain–computer interfaces in hemiplegic stroke survivors , 2012, Journal of neural engineering.

[45]  C. Braun,et al.  Combination of Brain-Computer Interface Training and Goal-Directed Physical Therapy in Chronic Stroke: A Case Report , 2010, Neurorehabilitation and neural repair.

[46]  Cuntai Guan,et al.  Optimizing Spatial Filters by Minimizing Within-Class Dissimilarities in Electroencephalogram-Based Brain–Computer Interface , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[47]  H. Zhang,et al.  A tensor-based scheme for stroke patients’ motor imagery EEG analysis in BCI-FES rehabilitation training , 2014, Journal of Neuroscience Methods.

[48]  J. Ushiba,et al.  Effects of neurofeedback training with an electroencephalogram-based brain-computer interface for hand paralysis in patients with chronic stroke: a preliminary case series study. , 2011, Journal of rehabilitation medicine.

[49]  Vivek Prabhakaran,et al.  Case report: post-stroke interventional BCI rehabilitation in an individual with preexisting sensorineural disability , 2014, Front. Neuroeng..

[50]  D. Farina,et al.  Detection of movement intention from single-trial movement-related cortical potentials , 2011, Journal of neural engineering.

[51]  P. Brown.  Cortical drives to human muscle: the Piper and related rhythms , 2000, Progress in Neurobiology.

[52]  P. Langhorne,et al.  Physical rehabilitation approaches for the recovery of function and mobility following stroke. , 2014, The Cochrane database of systematic reviews.

[53]  M. Hallett,et al.  What is the Bereitschaftspotential? , 2006, Clinical Neurophysiology.

[54]  Effie Chew,et al.  Is Motor‐Imagery Brain‐Computer Interface Feasible in Stroke Rehabilitation? , 2014, PM & R : the journal of injury, function, and rehabilitation.

[55]  Peter Langhorne,et al.  Predictors of upper limb recovery after stroke: a systematic review and meta-analysis , 2012, Clinical rehabilitation.

[56]  Jessica A Turner,et al.  Cerebral and cerebellar sensorimotor plasticity following motor imagery-based mental practice of a sequential movement. , 2004, Journal of rehabilitation research and development.

[57]  J. Millán,et al.  Detection of self-paced reaching movement intention from EEG signals , 2012, Front. Neuroeng..

[58]  H. Diener,et al.  Reorganization of Motor Execution Rather Than Preparation in Poststroke Hemiparesis , 2005, Stroke.

[59]  D. Farina,et al.  Precise temporal association between cortical potentials evoked by motor imagination and afference induces cortical plasticity , 2012, The Journal of physiology.

[60]  Peter Brown,et al.  The integration of cortical and behavioural dynamics during initial learning of a motor task , 2003, The European journal of neuroscience.

[61]  H. Slyter.  Guidelines for the management of patients with acute ischemic stroke. , 1995, Stroke.

[62]  Brittany M. Young,et al.  Changes in functional connectivity correlate with behavioral gains in stroke patients after therapy using a brain-computer interface device , 2014, Front. Neuroeng..

[63]  Cuntai Guan,et al.  Clinical study of neurorehabilitation in stroke using EEG-based motor imagery brain-computer interface with robotic feedback , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[64]  Alan C. Evans,et al.  Motor Learning Produces Parallel Dynamic Functional Changes during the Execution and Imagination of Sequential Foot Movements , 2002, NeuroImage.

[65]  Amr Kamel,et al.  Health Related Quality of Life in Stroke Survivors Measured by the Stroke Impact Scale , 2010 .

[66]  Edward S. Claflin,et al.  Emerging Treatments for Motor Rehabilitation After Stroke , 2015, The Neurohospitalist.

[67]  Alan J. Thompson,et al.  Functional significance of the ipsilateral hemisphere during movement of the affected hand after stroke , 2004, Experimental Neurology.

[68]  J. Ushiba,et al.  Modulation of event-related desynchronization during motor imagery with transcranial direct current stimulation (tDCS) in patients with chronic hemiparetic stroke , 2012, Experimental Brain Research.

[69]  David J. Reinkensmeyer,et al.  Performance Assessment of a Brain–Computer Interface Driven Hand Orthosis , 2014, Annals of Biomedical Engineering.

[70]  Shangkai Gao,et al.  Motor imagery based brain-computer interface: A study of the effect of positive and negative feedback , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[71]  P. Brown,et al.  The functional role of interhemispheric synchronization in the control of bimanual timing tasks , 2002, Experimental Brain Research.

[72]  Mark E. Dohring,et al.  Feasibility of a New Application of Noninvasive Brain Computer Interface (BCI): A Case Study of Training for Recovery of Volitional Motor Control After Stroke , 2009, Journal of neurologic physical therapy : JNPT.

[73]  C. Braun,et al.  Chronic stroke recovery after combined BCI training and physiotherapy: a case report. , 2011, Psychophysiology.

[74]  Ranganatha Sitaram,et al.  Brain-computer interfaces for neurorehabilitation. , 2013, Critical reviews in biomedical engineering.

[75]  Cuntai Guan,et al.  Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke , 2014, Front. Neuroeng..

[76]  G Pfurtscheller,et al.  Changes in central EEG activity in relation to voluntary movement. II. Hemiplegic patients. , 1980, Progress in brain research.

[77]  G. Chatrian,et al.  The blocking of the rolandic wicket rhythm and some central changes related to movement. , 1959, Electroencephalography and clinical neurophysiology.

[78]  Marie-Françoise Lucas,et al.  Optimization of wavelets for classification of movement-related cortical potentials generated by variation of force-related parameters , 2007, Journal of Neuroscience Methods.

[79]  Cuntai Guan,et al.  A clinical study of motor imagery-based brain-computer interface for upper limb robotic rehabilitation , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[80]  G. Pfurtscheller,et al.  Evaluation of event-related desynchronization (ERD) preceding and following voluntary self-paced movement. , 1979, Electroencephalography and clinical neurophysiology.

[81]  K H Mauritz,et al.  Multimodal EEG analysis in man suggests impairment-specific changes in movement-related electric brain activity after stroke. , 2000, Brain : a journal of neurology.

[82]  Cuntai Guan,et al.  Brain-Computer Interface in Stroke Rehabilitation , 2013, J. Comput. Sci. Eng..

[83]  G. Rozelle,et al.  Neurotherapy for stroke rehabilitation: A single case study , 1995, Biofeedback and self-regulation.

[84]  M. Hallett,et al.  Asymmetric spatiotemporal patterns of event-related desynchronization preceding voluntary sequential finger movements: a high-resolution EEG study , 2005, Clinical Neurophysiology.

[85]  G. Kwakkel,et al.  Predicting improvement in the upper paretic limb after stroke: a longitudinal prospective study. , 2007, Restorative neurology and neuroscience.

[86]  J. Ushiba,et al.  Relationship between event-related desynchronization and cortical excitability in healthy subjects and stroke patients. , 2013, The Tokai journal of experimental and clinical medicine.

[87]  Judy Trevena,et al.  Brain preparation before a voluntary action: Evidence against unconscious movement initiation , 2010, Consciousness and Cognition.

[88]  Lüder Deecke,et al.  High Resolution DC-EEG Analysis of the Bereitschaftspotential and Post Movement Onset Potentials Accompanying Uni- or Bilateral Voluntary Finger Movements , 2004, Brain Topography.

[89]  Fei Meng,et al.  A Minimal Set of Electrodes for Motor Imagery BCI to Control an Assistive Device in Chronic Stroke Subjects: A Multi-Session Study , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[90]  Cuntai Guan,et al.  A Large Clinical Study on the Ability of Stroke Patients to Use an EEG-Based Motor Imagery Brain-Computer Interface , 2011, Clinical EEG and neuroscience.

[91]  Cuntai Guan,et al.  Omitting the intra-session calibration in EEG-based brain computer interface used for stroke rehabilitation , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[92]  R Langton-Hewer,et al.  The hemiplegic arm after stroke: measurement and recovery. , 1983, Journal of neurology, neurosurgery, and psychiatry.

[93]  T. Murphy,et al.  Plasticity during stroke recovery: from synapse to behaviour , 2009, Nature Reviews Neuroscience.

[94]  Subashan Perera,et al.  Persisting Consequences of Stroke Measured by the Stroke Impact Scale , 2002, Stroke.

[95]  N. Birbaumer,et al.  Resting State Changes in Functional Connectivity Correlate With Movement Recovery for BCI and Robot-Assisted Upper-Extremity Training After Stroke , 2013, Neurorehabilitation and neural repair.

[96]  John Williamson,et al.  User-centered design in brain-computer interfaces - A case study , 2013, Artif. Intell. Medicine.

[97]  Cuntai Guan,et al.  A clinical study of motor imagery BCI performance in stroke by including calibration data from passive movement , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[98]  Ruth Dundas,et al.  Estimates of the Prevalence of Acute Stroke Impairments and Disability in a Multiethnic Population , 2001, Stroke.

[99]  M. Hallett,et al.  A high performance sensorimotor beta rhythm-based brain–computer interface associated with human natural motor behavior , 2008, Journal of neural engineering.

[100]  Cuntai Guan,et al.  Transcranial direct current stimulation and EEG-based motor imagery BCI for upper limb stroke rehabilitation , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.