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Zhao M, Wang G, Wang A, Cheng LJ, Lau Y. Robot-assisted distal training improves upper limb dexterity and function after stroke: a systematic review and meta-regression. Neurol Sci 2022; 43:1641-1657. [PMID: 35089447 DOI: 10.1007/s10072-022-05913-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 01/23/2022] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Stroke is one of the top 10 causes of death worldwide, and more than half of stroke patients face distal upper extremity dysfunction. Considering that robot-assisted training may be effective in improving distal upper extremity function, the review evaluated the effect of robot-assisted distal training on motor function, hand dexterity, and spasticity after stroke. METHODS Eleven databases were systematically searched for randomised controlled trials (RCTs) from inception until Aug 28, 2021. Meta-analysis and meta-regression were performed to investigate the overall effect and source of heterogeneity, respectively. RESULTS Twenty-two trials involving 758 participants were included in this systematic review. The overall effect of robot-assisted distal training on the motor function of the wrists and hands was significant improvement (MD = 3.92; 95% CI, 3.04-4.80; P < 0.001). The robot-assisted training had a significantly beneficial effect on other motor functions (MD = 2.84; 95% CI, 1.54-4.14; P < 0.001); dexterity (MD = 9.01; 95% CI, -12.07--5.95; P < 0.001), spasticity, upper extremity strength (SMD = 0.42; 95% CI, 0.07-0.78; P = 0.02) and activities of daily living (SMD = 0.70; 95% CI, 0.29-1.23; P < 0.001). A series of subgroup analyses showed preferable design and effective regime of training. Meta-regression indicated the statistically significant effect of the year of trial, country, and duration on the effectiveness of training. CONCLUSION Robot-assisted distal training has a significant effect on motor function, dexterity and spasticity of the upper extremity, compared to conventional therapy.
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Affiliation(s)
- Menglu Zhao
- The Affiliated Hospital of Qingdao University, Shandong, Qingdao, China
| | | | - Aimin Wang
- School of Nursing, Qingdao University, Qingdao, Shandong, China
| | - Ling Jie Cheng
- Health Systems and Behavioural Sciences Domain, Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Ying Lau
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Level 2, Block MD11, 10 Medical Drive, Singapore, 117597, Singapore.
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Angerhöfer C, Colucci A, Vermehren M, Hömberg V, Soekadar SR. Post-stroke Rehabilitation of Severe Upper Limb Paresis in Germany - Toward Long-Term Treatment With Brain-Computer Interfaces. Front Neurol 2021; 12:772199. [PMID: 34867760 PMCID: PMC8637332 DOI: 10.3389/fneur.2021.772199] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 10/29/2021] [Indexed: 12/03/2022] Open
Abstract
Severe upper limb paresis can represent an immense burden for stroke survivors. Given the rising prevalence of stroke, restoration of severe upper limb motor impairment remains a major challenge for rehabilitation medicine because effective treatment strategies are lacking. Commonly applied interventions in Germany, such as mirror therapy and impairment-oriented training, are limited in efficacy, demanding for new strategies to be found. By translating brain signals into control commands of external devices, brain-computer interfaces (BCIs) and brain-machine interfaces (BMIs) represent promising, neurotechnology-based alternatives for stroke patients with highly restricted arm and hand function. In this mini-review, we outline perspectives on how BCI-based therapy can be integrated into the different stages of neurorehabilitation in Germany to meet a long-term treatment approach: We found that it is most appropriate to start therapy with BCI-based neurofeedback immediately after early rehabilitation. BCI-driven functional electrical stimulation (FES) and BMI robotic therapy are well suited for subsequent post hospital curative treatment in the subacute stage. BCI-based hand exoskeleton training can be continued within outpatient occupational therapy to further improve hand function and address motivational issues in chronic stroke patients. Once the rehabilitation potential is exhausted, BCI technology can be used to drive assistive devices to compensate for impaired function. However, there are several challenges yet to overcome before such long-term treatment strategies can be implemented within broad clinical application: 1. developing reliable BCI systems with better usability; 2. conducting more research to improve BCI training paradigms and 3. establishing reliable methods to identify suitable patients.
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Affiliation(s)
- Cornelius Angerhöfer
- Clinical Neurotechnology Lab, Department of Psychiatry and Neurosciences, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Annalisa Colucci
- Clinical Neurotechnology Lab, Department of Psychiatry and Neurosciences, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Mareike Vermehren
- Clinical Neurotechnology Lab, Department of Psychiatry and Neurosciences, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Volker Hömberg
- Department of Neurology, SRH Gesundheitszentrum Bad Wimpfen GmbH, Bad Wimpfen, Germany
| | - Surjo R Soekadar
- Clinical Neurotechnology Lab, Department of Psychiatry and Neurosciences, Charité-Universitätsmedizin Berlin, Berlin, Germany
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Qiu W, Yang B, Ma J, Gao S, Zhu Y, Wang W. The Paradigm Design of a Novel 2-class Unilateral Upper Limb Motor Imagery Tasks and its EEG Signal Classification. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:152-155. [PMID: 34891260 DOI: 10.1109/embc46164.2021.9630837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Multitasking motor imagery (MI) of the unilateral upper limb is potentially more valuable in stroke rehabilitation than the current conventional MI in both hands. In this paper, a novel experimental paradigm was designed to imagine two motions of unilateral upper limb, which is hand gripping and releasing, and elbow reciprocating left and right. During this experiment, the electroencephalogram (EEG) signals were collected from 10 subjects. The time and frequency domains of the EEG signals were analyzed and visualized, indicating the presence of different Event-Related Desynchronization (ERD) or Event-Related Synchronization (ERS) for the two tasks. Then the two tasks were classified through three different EEG decoding methods, in which the optimized convolutional neural network (CNN) based on FBCNet achieved an average accuracy of 67.8%, obtaining a good recognition result. This work not only can advance the studies of MI decoding of unilateral upper limb, but also can provide a basis for better upper limb stroke rehabilitation in MI-BCI.
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Vasilyev AN, Nuzhdin YO, Kaplan AY. Does Real-Time Feedback Affect Sensorimotor EEG Patterns in Routine Motor Imagery Practice? Brain Sci 2021; 11:brainsci11091234. [PMID: 34573253 PMCID: PMC8469546 DOI: 10.3390/brainsci11091234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 09/16/2021] [Accepted: 09/16/2021] [Indexed: 11/23/2022] Open
Abstract
Background. Motor imagery engages much of the same neural circuits as an overt movement. Therefore, the mental rehearsal of movements is often used to supplement physical training and might aid motor neurorehabilitation after stroke. One attempt to capture the brain’s involvement in imagery involves the use, as a marker, of the depression or event-related desynchronization (ERD) of thalamocortical sensorimotor rhythms found in a human electroencephalogram (EEG). Using fast real-time processing, it is possible to make the subject aware of their own brain reactions or—even better—to turn them into actions through a technology called the brain–computer interface (BCI). However, it remains unclear whether BCI-enabled imagery facilitates a stronger or qualitatively different brain response compared to the open-loop training. Methods. Seven healthy volunteers who were experienced in both closed and open-loop motor imagery took part in six experimental sessions over a period of 4.5 months, in which they performed kinesthetic imagery of a previously known set of finger and arm movements with simultaneous 30-channel EEG acquisition. The first and the last session mostly consisted of feedback trials in which the subjects were presented with the classification results of the EEG patterns in real time; during the other sessions, no feedback was provided. Spatiotemporal and amplitude features of the ERD patterns concomitant with imagery were compared across experimental days and between feedback conditions using linear mixed-effects modeling. Results. The main spatial sources of ERD appeared to be highly stable across the six experimental days, remaining nearly identical in five of seven subjects (Pearson’s ρ > 0.94). Only in one subject did the spatial pattern of activation statistically significantly differ (p = 0.009) between the feedback and no-feedback conditions. Real-time visual feedback delivered through the BCI did not significantly increase the ERD strength. Conclusion. The results imply that the potential benefits of MI could be yielded by well-habituated subjects with a simplified open-loop setup, e.g., through at-home self-practice.
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Affiliation(s)
- Anatoly N. Vasilyev
- Department of Human and Animal Physiology, Faculty of Biology, M.V. Lomonosov Moscow State University, 119234 Moscow, Russia; (Y.O.N.); (A.Y.K.)
- MEG Center, Moscow State University of Psychology and Education, 123290 Moscow, Russia
- Correspondence:
| | - Yury O. Nuzhdin
- Department of Human and Animal Physiology, Faculty of Biology, M.V. Lomonosov Moscow State University, 119234 Moscow, Russia; (Y.O.N.); (A.Y.K.)
| | - Alexander Y. Kaplan
- Department of Human and Animal Physiology, Faculty of Biology, M.V. Lomonosov Moscow State University, 119234 Moscow, Russia; (Y.O.N.); (A.Y.K.)
- Center for Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
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Nann M, Haslacher D, Colucci A, Eskofier B, von Tscharner V, Soekadar SR. Heart rate variability predicts decline in sensorimotor rhythm control. J Neural Eng 2021; 18. [PMID: 34229308 DOI: 10.1088/1741-2552/ac1177] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 07/06/2021] [Indexed: 11/11/2022]
Abstract
Objective.Voluntary control of sensorimotor rhythms (SMRs, 8-12 Hz) can be used for brain-computer interface (BCI)-based operation of an assistive hand exoskeleton, e.g. in finger paralysis after stroke. To gain SMR control, stroke survivors are usually instructed to engage in motor imagery (MI) or to attempt moving the paralyzed fingers resulting in task- or event-related desynchronization (ERD) of SMR (SMR-ERD). However, as these tasks are cognitively demanding, especially for stroke survivors suffering from cognitive impairments, BCI control performance can deteriorate considerably over time. Therefore, it would be important to identify biomarkers that predict decline in BCI control performance within an ongoing session in order to optimize the man-machine interaction scheme.Approach.Here we determine the link between BCI control performance over time and heart rate variability (HRV). Specifically, we investigated whether HRV can be used as a biomarker to predict decline of SMR-ERD control across 17 healthy participants using Granger causality. SMR-ERD was visually displayed on a screen. Participants were instructed to engage in MI-based SMR-ERD control over two consecutive runs of 8.5 min each. During the 2nd run, task difficulty was gradually increased.Main results.While control performance (p= .18) and HRV (p= .16) remained unchanged across participants during the 1st run, during the 2nd run, both measures declined over time at high correlation (performance: -0.61%/10 s,p= 0; HRV: -0.007 ms/10 s,p< .001). We found that HRV exhibited predictive characteristics with regard to within-session BCI control performance on an individual participant level (p< .001).Significance.These results suggest that HRV can predict decline in BCI performance paving the way for adaptive BCI control paradigms, e.g. to individualize and optimize assistive BCI systems in stroke.
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Affiliation(s)
- Marius Nann
- Applied Neurotechnology Lab, Department of Psychiatry and Psychotherapy, University Hospital of Tübingen, Tübingen, Germany.,Clinical Neurotechnology Lab, Neuroscience Research Center (NWFZ), Department of Psychiatry and Psychotherapy, Charité - University Medicine Berlin, Berlin, Germany
| | - David Haslacher
- Clinical Neurotechnology Lab, Neuroscience Research Center (NWFZ), Department of Psychiatry and Psychotherapy, Charité - University Medicine Berlin, Berlin, Germany
| | - Annalisa Colucci
- Clinical Neurotechnology Lab, Neuroscience Research Center (NWFZ), Department of Psychiatry and Psychotherapy, Charité - University Medicine Berlin, Berlin, Germany
| | - Bjoern Eskofier
- Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | | | - Surjo R Soekadar
- Clinical Neurotechnology Lab, Neuroscience Research Center (NWFZ), Department of Psychiatry and Psychotherapy, Charité - University Medicine Berlin, Berlin, Germany
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Bobrova EV, Reshetnikova VV, Vershinina EA, Grishin AA, Bobrov PD, Frolov AA, Gerasimenko YP. Success of Hand Movement Imagination Depends on Personality Traits, Brain Asymmetry, and Degree of Handedness. Brain Sci 2021; 11:853. [PMID: 34202413 PMCID: PMC8301954 DOI: 10.3390/brainsci11070853] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 06/23/2021] [Accepted: 06/23/2021] [Indexed: 12/05/2022] Open
Abstract
Brain-computer interfaces (BCIs), based on motor imagery, are increasingly used in neurorehabilitation. However, some people cannot control BCI, predictors of this are the features of brain activity and personality traits. It is not known whether the success of BCI control is related to interhemispheric asymmetry. The study was conducted on 44 BCI-naive subjects and included one BCI session, EEG-analysis, 16PF Cattell Questionnaire, estimation of latent left-handedness, and of subjective complexity of real and imagery movements. The success of brain states recognition during imagination of left hand (LH) movement compared to the rest is higher in reserved, practical, skeptical, and not very sociable individuals. Extraversion, liveliness, and dominance are significant for the imagination of right hand (RH) movements in "pure" right-handers, and sensitivity in latent left-handers. Subjective complexity of real LH and of imagery RH movements correlates with the success of brain states recognition in the imagination of movement of LH compared to RH and depends on the level of handedness. Thus, the level of handedness is the factor influencing the success of BCI control. The data are supposed to be connected with hemispheric differences in motor control, lateralization of dopamine, and may be important for rehabilitation of patients after a stroke.
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Affiliation(s)
- Elena V. Bobrova
- Pavlov Institute of Physiology of the Russian Academy of Sciences, 199034 Saint-Petersburg, Russia; (V.V.R.); (E.A.V.); (A.A.G.); (Y.P.G.)
| | - Varvara V. Reshetnikova
- Pavlov Institute of Physiology of the Russian Academy of Sciences, 199034 Saint-Petersburg, Russia; (V.V.R.); (E.A.V.); (A.A.G.); (Y.P.G.)
| | - Elena A. Vershinina
- Pavlov Institute of Physiology of the Russian Academy of Sciences, 199034 Saint-Petersburg, Russia; (V.V.R.); (E.A.V.); (A.A.G.); (Y.P.G.)
| | - Alexander A. Grishin
- Pavlov Institute of Physiology of the Russian Academy of Sciences, 199034 Saint-Petersburg, Russia; (V.V.R.); (E.A.V.); (A.A.G.); (Y.P.G.)
| | - Pavel D. Bobrov
- Institute of Translational Medicine of Pirogov of Russian National Research Medical University, 117997 Moscow, Russia; (P.D.B.); (A.A.F.)
- Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, 117485 Moscow, Russia
| | - Alexander A. Frolov
- Institute of Translational Medicine of Pirogov of Russian National Research Medical University, 117997 Moscow, Russia; (P.D.B.); (A.A.F.)
- Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, 117485 Moscow, Russia
| | - Yury P. Gerasimenko
- Pavlov Institute of Physiology of the Russian Academy of Sciences, 199034 Saint-Petersburg, Russia; (V.V.R.); (E.A.V.); (A.A.G.); (Y.P.G.)
- Department of Physiology and Biophysics, University of Louisville, Louisville, KY 40292, USA
- Kentucky Spinal Cord Injury Research Center, Frazier Rehab Institute, University of Louisville, UofL Health, Louisville, KY 40202, USA
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Bobrov PD, Biryukova EV, Polyaev BA, Lajsheva OA, Usachjova EL, Sokolova AV, Mikhailova DI, Dement'eva KN, Fedotova IR. Rehabilitation of patients with cerebral palsy using hand exoskeleton controlled by brain-computer interface. BULLETIN OF RUSSIAN STATE MEDICAL UNIVERSITY 2020. [DOI: 10.24075/brsmu.2020.047] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Cerebral palsy (CP) is one of the most severe central nervous system diseases in childhood associated with motor impairment. The study was aimed to assess the efficiency of the complex comprising brain-computer interface (BCI) and hand exoskeleton as an instrument for the motor function recovery in patients with CP complementing the essential therapy. The Fugl-Meyer Assessment scale, ARAT test and Jebsen–Taylor function test were used in 14 children and adolescents for the motor function improvement assessment after the therapy complemented by 7–10 BCI-exoskeleton based procedures. The EEG mu-rhythm sources properties during the motor imagery BCI control were studied. After the procedures completion, the significant improvement of the Fugl-Meyer Assessment scale score (7 (2; 11) for hand active movements; 4.5 (1; 6) for proximal arm and 2.5 (0; 5) for hand), ARAT test score (7.5 (1; 31) for total score, 1.5 (0; 12) for grasp movement and 1.5 (0; 8) for grip movement), as well as significantly different from the zero execution time reduction in three out of seven Jabsen–Taylor function test items (–1 (–4.13; 0.25) for simulated feeding; –1 (–2; 0) for moving light and heavy cans) were identified. The average BCI detection level was 0.51 (0.45; 0.54) (max = 0.70). In most EEG recordings the mu-rhythm sources were detected, both for intact and affected hemispheres. The mu-rhythm desynchronization associated with motor imagery was observed, significantly affecting the BCI accuracy. The results obtained indicate that the use of BCI-exoskeleton complex effectively complements the standard rehabilitation methods for children with CP, and suggest that its clinical effectiveness in individuals with CP may be proven by enrollment of more patients.
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Affiliation(s)
- PD Bobrov
- Pirogov Russian National Research Medical University, Moscow, Russia; Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, Moscow, Russia
| | - EV Biryukova
- Pirogov Russian National Research Medical University, Moscow, Russia; Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, Moscow, Russia
| | - BA Polyaev
- Pirogov Russian National Research Medical University, Moscow, Russia
| | - OA Lajsheva
- Pirogov Russian National Research Medical University, Moscow, Russia; Russian Children's Clinical Hospital of Pirogov Russian National Research Medical University, Moscow, Russia
| | - EL Usachjova
- Russian Children's Clinical Hospital of Pirogov Russian National Research Medical University, Moscow, Russia
| | - AV Sokolova
- Russian Children's Clinical Hospital of Pirogov Russian National Research Medical University, Moscow, Russia
| | - DI Mikhailova
- Russian Children's Clinical Hospital of Pirogov Russian National Research Medical University, Moscow, Russia
| | - KN Dement'eva
- Russian Children's Clinical Hospital of Pirogov Russian National Research Medical University, Moscow, Russia
| | - IR Fedotova
- Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, Moscow, Russia
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Harnarinesingh RE, Syan CS. Investigation of the mirrored-word reading paradigm for BCI implementation. BIOMED ENG-BIOMED TE 2019; 64:325-337. [DOI: 10.1515/bmt-2017-0223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Accepted: 06/01/2018] [Indexed: 11/15/2022]
Abstract
Abstract
Brain-computer interface (BCI) applications such as keyboard control and vehicular navigation present significant assistive merit for disabled individuals. However, there are limitations associated with BCI paradigms which restrict a wider adoption of BCI technology. For example, rapid serial visual presentation (RSVP) paradigms can induce seizures in photosensitive epileptic subjects. This paper evaluates the novel mirrored-word reading paradigm (MWRP) for BCI implementation using an offline experimental study. The offline study obtained an average single-trial classification accuracy of 74.10%. The results also demonstrate that the use of multiple trials for classification can increase the accuracy as is common with BCIs. The developed MWRP-based BCI also utilized a low presentation frequency which averts the possibility of paradigm induced photosensitivity. However, there are multiple avenues for future work. The MWRP can be implemented in the online format for real-time device control. For example, a vehicular application platform can be used where the word orientation represents directions for travel. The MWRP can also be investigated across a wider range of stimulus presentation parameters such as timing, color and stimulus size. Such studies can be used to suggest further improvements to the paradigm which can enhance its applicability for online device control.
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Frolov AA, Kozlovskaya IB, Biryukova EV, Bobrov PD. Use of Robotic Devices in Post-Stroke Rehabilitation. ACTA ACUST UNITED AC 2018. [DOI: 10.1007/s11055-018-0668-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Choi H, Lee S, Lee J, Min K, Lim S, Park J, Ahn KH, Kim IY, Lee KM, Jang DP. Long-term evaluation and feasibility study of the insulated screw electrode for ECoG recording. J Neurosci Methods 2018; 308:261-268. [DOI: 10.1016/j.jneumeth.2018.06.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Revised: 06/08/2018] [Accepted: 06/27/2018] [Indexed: 10/28/2022]
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Frolov AA, Mokienko O, Lyukmanov R, Biryukova E, Kotov S, Turbina L, Nadareyshvily G, Bushkova Y. Post-stroke Rehabilitation Training with a Motor-Imagery-Based Brain-Computer Interface (BCI)-Controlled Hand Exoskeleton: A Randomized Controlled Multicenter Trial. Front Neurosci 2017; 11:400. [PMID: 28775677 PMCID: PMC5517482 DOI: 10.3389/fnins.2017.00400] [Citation(s) in RCA: 140] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Accepted: 06/26/2017] [Indexed: 11/20/2022] Open
Abstract
Repeated use of brain-computer interfaces (BCIs) providing contingent sensory feedback of brain activity was recently proposed as a rehabilitation approach to restore motor function after stroke or spinal cord lesions. However, there are only a few clinical studies that investigate feasibility and effectiveness of such an approach. Here we report on a placebo-controlled, multicenter clinical trial that investigated whether stroke survivors with severe upper limb (UL) paralysis benefit from 10 BCI training sessions each lasting up to 40 min. A total of 74 patients participated: median time since stroke is 8 months, 25 and 75% quartiles [3.0; 13.0]; median severity of UL paralysis is 4.5 points [0.0; 30.0] as measured by the Action Research Arm Test, ARAT, and 19.5 points [11.0; 40.0] as measured by the Fugl-Meyer Motor Assessment, FMMA. Patients in the BCI group (n = 55) performed motor imagery of opening their affected hand. Motor imagery-related brain electroencephalographic activity was translated into contingent hand exoskeleton-driven opening movements of the affected hand. In a control group (n = 19), hand exoskeleton-driven opening movements of the affected hand were independent of brain electroencephalographic activity. Evaluation of the UL clinical assessments indicated that both groups improved, but only the BCI group showed an improvement in the ARAT's grasp score from 0 [0.0; 14.0] to 3.0 [0.0; 15.0] points (p < 0.01) and pinch scores from 0.0 [0.0; 7.0] to 1.0 [0.0; 12.0] points (p < 0.01). Upon training completion, 21.8% and 36.4% of the patients in the BCI group improved their ARAT and FMMA scores respectively. The corresponding numbers for the control group were 5.1% (ARAT) and 15.8% (FMMA). These results suggests that adding BCI control to exoskeleton-assisted physical therapy can improve post-stroke rehabilitation outcomes. Both maximum and mean values of the percentage of successfully decoded imagery-related EEG activity, were higher than chance level. A correlation between the classification accuracy and the improvement in the upper extremity function was found. An improvement of motor function was found for patients with different duration, severity and location of the stroke.
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Affiliation(s)
- Alexander A. Frolov
- Research Institute of Translational Medicine, Pirogov Russian National Research Medical UniversityMoscow, Russia
- Laboratory of Mathematical Neurobiology of Learning of Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of SciencesMoscow, Russia
| | - Olesya Mokienko
- Research Institute of Translational Medicine, Pirogov Russian National Research Medical UniversityMoscow, Russia
- Department of Neurorehabilitation and Physiotherapy of Research Center of Neurology, Russian Academy of Medical SciencesMoscow, Russia
| | - Roman Lyukmanov
- Research Institute of Translational Medicine, Pirogov Russian National Research Medical UniversityMoscow, Russia
- Department of Neurorehabilitation and Physiotherapy of Research Center of Neurology, Russian Academy of Medical SciencesMoscow, Russia
| | - Elena Biryukova
- Research Institute of Translational Medicine, Pirogov Russian National Research Medical UniversityMoscow, Russia
- Laboratory of Mathematical Neurobiology of Learning of Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of SciencesMoscow, Russia
| | - Sergey Kotov
- Department of Neurology, Vladimirsky Moscow Regional Research Clinical InstituteMoscow, Russia
| | - Lydia Turbina
- Department of Neurology, Vladimirsky Moscow Regional Research Clinical InstituteMoscow, Russia
| | - Georgy Nadareyshvily
- Medical Faculty, Pirogov Russian National Research Medical UniversityMoscow, Russia
| | - Yulia Bushkova
- Research Institute of Cerebrovascular Pathology and Stroke, Pirogov Russian National Research Medical UniversityMoscow, Russia
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Osuagwu BA, Zych M, Vuckovic A. Is Implicit Motor Imagery a Reliable Strategy for a Brain-Computer Interface? IEEE Trans Neural Syst Rehabil Eng 2017; 25:2239-2248. [PMID: 28682260 DOI: 10.1109/tnsre.2017.2712707] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Explicit motor imagery (eMI) is a widely used brain-computer interface (BCI) paradigm, but not everybody can accomplish this task. Here, we propose a BCI based on implicit motor imagery (iMI). We compared classification accuracy between eMI and iMI of hands. Fifteen able-bodied people were asked to judge the laterality of hand images presented on a computer screen in a lateral or medial orientation. This judgment task is known to require mental rotation of a person's own hands, which in turn is thought to involve iMI. The subjects were also asked to perform eMI of the hands. Their electroencephalography was recorded. Linear classifiers were designed based on common spatial patterns. For discrimination between left hand and right hand, the classifier achieved maximum of 81 ± 8% accuracy for eMI and 83 ± 3% for iMI. These results show that iMI can be used to achieve similar classification accuracy as eMI. Additional classification was performed between iMI in medial and lateral orientations of a single hand; the classifier achieved 81 ± 7% for the left hand and 78 ± 7% for the right hand, which indicate distinctive spatial patterns of cortical activity for iMI of a single hand in different directions. These results suggest that a special BCI based on iMI may be constructed, for people who cannot perform explicit imagination, for rehabilitation of movement, or for treatment of bodily spatial neglect.
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