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      BCI-Based Rehabilitation on the Stroke in Sequela Stage

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          Abstract

          Background

          Stroke is the leading cause of serious and long-term disability worldwide. Survivors may recover some motor functions after rehabilitation therapy. However, many stroke patients missed the best time period for recovery and entered into the sequela stage of chronic stroke.

          Method

          Studies have shown that motor imagery- (MI-) based brain-computer interface (BCI) has a positive effect on poststroke rehabilitation. This study used both virtual limbs and functional electrical stimulation (FES) as feedback to provide patients with a closed-loop sensorimotor integration for motor rehabilitation. An MI-based BCI system acquired, analyzed, and classified motor attempts from electroencephalogram (EEG) signals. The FES system would be activated if the BCI detected that the user was imagining wrist dorsiflexion on the instructed side of the body. Sixteen stroke patients in the sequela stage were randomly assigned to a BCI group and a control group. All of them participated in rehabilitation training for four weeks and were assessed by the Fugl-Meyer Assessment (FMA) of motor function.

          Results

          The average improvement score of the BCI group was 3.5, which was higher than that of the control group (0.9). The active EEG patterns of the four patients in the BCI group whose FMA scores increased gradually became centralized and shifted to sensorimotor areas and premotor areas throughout the study.

          Conclusions

          Study results showed evidence that patients in the BCI group achieved larger functional improvements than those in the control group and that the BCI-FES system is effective in restoring motor function to upper extremities in stroke patients. This study provides a more autonomous approach than traditional treatments used in stroke rehabilitation.

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          Most cited references56

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          Event-related EEG/MEG synchronization and desynchronization: basic principles.

          An internally or externally paced event results not only in the generation of an event-related potential (ERP) but also in a change in the ongoing EEG/MEG in form of an event-related desynchronization (ERD) or event-related synchronization (ERS). The ERP on the one side and the ERD/ERS on the other side are different responses of neuronal structures in the brain. While the former is phase-locked, the latter is not phase-locked to the event. The most important difference between both phenomena is that the ERD/ERS is highly frequency band-specific, whereby either the same or different locations on the scalp can display ERD and ERS simultaneously. Quantification of ERD/ERS in time and space is demonstrated on data from a number of movement experiments.
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            THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS

            R Fisher (1936)
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              The post-stroke hemiplegic patient. 1. a method for evaluation of physical performance.

              A system for evaluation of motor function, balance, some sensation qualities and joint function in hemiplegic patients is described in detail. The system applies a cumulative numerical score. A series of hemiplegic patients has been followed from within one week post-stroke and throughout one year. When initially nearly flaccid hemiparalysis prevails, the motor recovery, if any occur, follows a definable course. The findings in this study substantiate the validity of ontogenetic principles as applicable to the assessment of motor behaviour in hemiplegic patients, and foocus the importance of early therapeutic measures against contractures.
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                Author and article information

                Contributors
                Journal
                Neural Plast
                Neural Plast
                NP
                Neural Plasticity
                Hindawi
                2090-5904
                1687-5443
                2020
                13 December 2020
                : 2020
                : 8882764
                Affiliations
                1Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China
                2Department of Rehabilitation, Huashan Hospital, Fudan University, Shanghai, China
                3Guger Technologies OG, Austria
                4Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex CO4 3SQ, UK
                5Skolkowo Institute of Science and Technology (SKOLTECH), 143026 Moscow, Russia
                6Systems Research Institute PAS, Warsaw, Poland
                7Nicolaus Copernicus University (UMK), Torun, Poland
                8Institute for Neural Computation and Institute of Engineering in Medicine, University of California San Diego, La Jolla, CA, USA
                Author notes

                Academic Editor: Jianzhong Su

                Author information
                https://orcid.org/0000-0002-6133-5491
                https://orcid.org/0000-0002-4516-4629
                Article
                10.1155/2020/8882764
                7752268
                33414824
                05b7d02e-1f94-4e35-9c67-88d8dbc9feef
                Copyright © 2020 Yangyang Miao et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 28 September 2020
                : 25 November 2020
                : 30 November 2020
                Funding
                Funded by: Shanghai Education Development Foundation
                Award ID: 19SG25
                Funded by: Shanghai Municipal Education Commission
                Funded by: Programme of Introducing Talents of Discipline to Universities (the 111 Project)
                Award ID: B17017
                Funded by: National Natural Science Foundation of China
                Award ID: 91420302
                Award ID: 61773164
                Award ID: 61573142
                Funded by: National Key Research and Development Program
                Award ID: 2018YFC2002301
                Award ID: 2018YFC2002300
                Award ID: 2017YFB13003002
                Categories
                Research Article

                Neurosciences
                Neurosciences

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