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Höhler C, Trigili E, Astarita D, Hermsdörfer J, Jahn K, Krewer C. The efficacy of hybrid neuroprostheses in the rehabilitation of upper limb impairment after stroke, a narrative and systematic review with a meta-analysis. Artif Organs 2024; 48:232-253. [PMID: 37548237 DOI: 10.1111/aor.14618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 06/30/2023] [Accepted: 07/17/2023] [Indexed: 08/08/2023]
Abstract
BACKGROUND Paresis of the upper limb (UL) is the most frequent impairment after a stroke. Hybrid neuroprostheses, i.e., the combination of robots and electrical stimulation, have emerged as an option to treat these impairments. METHODS To give an overview of existing devices, their features, and how they are linked to clinical metrics, four different databases were systematically searched for studies on hybrid neuroprostheses for UL rehabilitation after stroke. The evidence on the efficacy of hybrid therapies was synthesized. RESULTS Seventy-three studies were identified, introducing 32 hybrid systems. Among the most recent devices (n = 20), most actively reinforce movement (3 passively) and are typical exoskeletons (3 end-effectors). If classified according to the International Classification of Functioning, Disability and Health, systems for proximal support are expected to affect body structures and functions, while the activity and participation level are targeted when applying Functional Electrical Stimulation distally plus the robotic component proximally. The meta-analysis reveals a significant positive effect on UL functions (p < 0.001), evident in a 7.8-point Mdiff between groups in the Fugl-Meyer assessment. This positive effect remains at the 3-month follow-up (Mdiff = 8.4, p < 0.001). CONCLUSIONS Hybrid neuroprostheses have a positive effect on UL recovery after stroke, with effects persisting at least three months after the intervention. Non-significant studies were those with the shortest intervention periods and the oldest patients. Improvements in UL functions are not only present in the subacute phase after stroke but also in long-term chronic stages. In addition to further technical development, more RCTs are needed to make assumptions about the determinants of successful therapy.
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Affiliation(s)
- Chiara Höhler
- Research Department, Schoen Clinic Bad Aibling, Bad Aibling, Germany
- Chair of Human Movement Science, Faculty of Sport and Health Science, Technical University Munich, Munich, Germany
| | - Emilio Trigili
- The Biorobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Davide Astarita
- The Biorobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Joachim Hermsdörfer
- Chair of Human Movement Science, Faculty of Sport and Health Science, Technical University Munich, Munich, Germany
| | - Klaus Jahn
- Research Department, Schoen Clinic Bad Aibling, Bad Aibling, Germany
- German Center for Vertigo and Balance Disorders (DSGZ), Ludwig-Maximilians University of Munich (LMU), Munich, Germany
| | - Carmen Krewer
- Research Department, Schoen Clinic Bad Aibling, Bad Aibling, Germany
- Chair of Human Movement Science, Faculty of Sport and Health Science, Technical University Munich, Munich, Germany
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Brunner I, Lundquist CB, Pedersen AR, Spaich EG, Dosen S, Savic A. Brain computer interface training with motor imagery and functional electrical stimulation for patients with severe upper limb paresis after stroke: a randomized controlled pilot trial. J Neuroeng Rehabil 2024; 21:10. [PMID: 38245782 PMCID: PMC10799379 DOI: 10.1186/s12984-024-01304-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 01/09/2024] [Indexed: 01/22/2024] Open
Abstract
BACKGROUND Restorative Brain-Computer Interfaces (BCI) that combine motor imagery with visual feedback and functional electrical stimulation (FES) may offer much-needed treatment alternatives for patients with severely impaired upper limb (UL) function after a stroke. OBJECTIVES This study aimed to examine if BCI-based training, combining motor imagery with FES targeting finger/wrist extensors, is more effective in improving severely impaired UL motor function than conventional therapy in the subacute phase after stroke, and if patients with preserved cortical-spinal tract (CST) integrity benefit more from BCI training. METHODS Forty patients with severe UL paresis (< 13 on Action Research Arm Test (ARAT) were randomized to either a 12-session BCI training as part of their rehabilitation or conventional UL rehabilitation. BCI sessions were conducted 3-4 times weekly for 3-4 weeks. At baseline, Transcranial Magnetic Stimulation (TMS) was performed to examine CST integrity. The main endpoint was the ARAT at 3 months post-stroke. A binominal logistic regression was conducted to examine the effect of treatment group and CST integrity on achieving meaningful improvement. In the BCI group, electroencephalographic (EEG) data were analyzed to investigate changes in event-related desynchronization (ERD) during the course of therapy. RESULTS Data from 35 patients (15 in the BCI group and 20 in the control group) were analyzed at 3-month follow-up. Few patients (10/35) improved above the minimally clinically important difference of 6 points on ARAT, 5/15 in the BCI group, 5/20 in control. An independent-samples Mann-Whitney U test revealed no differences between the two groups, p = 0.382. In the logistic regression only CST integrity was a significant predictor for improving UL motor function, p = 0.007. The EEG analysis showed significant changes in ERD of the affected hemisphere and its lateralization only during unaffected UL motor imagery at the end of the therapy. CONCLUSION This is the first RCT examining BCI training in the subacute phase where only patients with severe UL paresis were included. Though more patients in the BCI group improved relative to the group size, the difference between the groups was not significant. In the present study, preserved CTS integrity was much more vital for UL improvement than which type of intervention the patients received. Larger studies including only patients with some preserved CST integrity should be attempted.
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Affiliation(s)
- Iris Brunner
- Department of Clinical Medicine, Hammel Neurocenter and University Hospital, Aarhus University, Voldbyvej 12, 8450, Hammel, Denmark.
| | | | - Asger Roer Pedersen
- University Research Clinic for Innovative Patient Pathways, Diagnostic Centre, Silkeborg Regional Hospital, 8600, Silkeborg, Denmark
| | - Erika G Spaich
- Department of Health Science and Technology, Aalborg University, 9220, Aalborg, Denmark
| | - Strahinja Dosen
- Department of Health Science and Technology, Aalborg University, 9220, Aalborg, Denmark
| | - Andrej Savic
- Science and Research Centre, University of Belgrade-School of Electrical Engineering, Belgrade, 11000, Serbia
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Fasipe G, Goršič M, Rahman MH, Rammer J. Community mobility and participation assessment of manual wheelchair users: a review of current techniques and challenges. Front Hum Neurosci 2024; 17:1331395. [PMID: 38249574 PMCID: PMC10796510 DOI: 10.3389/fnhum.2023.1331395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 12/11/2023] [Indexed: 01/23/2024] Open
Abstract
According to the World Health Organization, hundreds of individuals commence wheelchair use daily, often due to an injury such as spinal cord injury or through a condition such as a stroke. However, manual wheelchair users typically experience reductions in individual community mobility and participation. In this review, articles from 2017 to 2023 were reviewed to identify means of measuring community mobility and participation of manual wheelchair users, factors that can impact these aspects, and current rehabilitation techniques for improving them. The selected articles document current best practices utilizing self-surveys, in-clinic assessments, and remote tracking through GPS and accelerometer data, which rehabilitation specialists can apply to track their patients' community mobility and participation accurately. Furthermore, rehabilitation methods such as wheelchair training programs, brain-computer interface triggered functional electric stimulation therapy, and community-based rehabilitation programs show potential to improve the community mobility and participation of manual wheelchair users. Recommendations were made to highlight potential avenues for future research.
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Affiliation(s)
- Grace Fasipe
- Department of Biomedical Engineering, College of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
| | - Maja Goršič
- Department of Biomedical Engineering, College of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
| | - Mohammad Habibur Rahman
- Department of Biomedical Engineering, College of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
- Department of Mechanical Engineering, College of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
| | - Jacob Rammer
- Department of Biomedical Engineering, College of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
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4
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Mang J, Xu Z, Qi Y, Zhang T. Favoring the cognitive-motor process in the closed-loop of BCI mediated post stroke motor function recovery: challenges and approaches. Front Neurorobot 2023; 17:1271967. [PMID: 37881517 PMCID: PMC10595019 DOI: 10.3389/fnbot.2023.1271967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 09/08/2023] [Indexed: 10/27/2023] Open
Abstract
The brain-computer interface (BCI)-mediated rehabilitation is emerging as a solution to restore motor skills in paretic patients after stroke. In the human brain, cortical motor neurons not only fire when actions are carried out but are also activated in a wired manner through many cognitive processes related to movement such as imagining, perceiving, and observing the actions. Moreover, the recruitment of motor cortexes can usually be regulated by environmental conditions, forming a closed-loop through neurofeedback. However, this cognitive-motor control loop is often interrupted by the impairment of stroke. The requirement to bridge the stroke-induced gap in the motor control loop is promoting the evolution of the BCI-based motor rehabilitation system and, notably posing many challenges regarding the disease-specific process of post stroke motor function recovery. This review aimed to map the current literature surrounding the new progress in BCI-mediated post stroke motor function recovery involved with cognitive aspect, particularly in how it refired and rewired the neural circuit of motor control through motor learning along with the BCI-centric closed-loop.
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Affiliation(s)
- Jing Mang
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Zhuo Xu
- Department of Rehabilitation, China-Japan Union Hospital of Jilin University, Changchun, China
| | - YingBin Qi
- Department of Neurology, Jilin Province People's Hospital, Changchun, China
| | - Ting Zhang
- Rehabilitation Therapeutics, School of Nursing, Jilin University, Changchun, China
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Behboodi A, Lee WA, Hinchberger VS, Damiano DL. Determining optimal mobile neurofeedback methods for motor neurorehabilitation in children and adults with non-progressive neurological disorders: a scoping review. J Neuroeng Rehabil 2022; 19:104. [PMID: 36171602 PMCID: PMC9516814 DOI: 10.1186/s12984-022-01081-9] [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: 03/25/2022] [Accepted: 09/08/2022] [Indexed: 11/22/2022] Open
Abstract
Background Brain–computer interfaces (BCI), initially designed to bypass the peripheral motor system to externally control movement using brain signals, are additionally being utilized for motor rehabilitation in stroke and other neurological disorders. Also called neurofeedback training, multiple approaches have been developed to link motor-related cortical signals to assistive robotic or electrical stimulation devices during active motor training with variable, but mostly positive, functional outcomes reported. Our specific research question for this scoping review was: for persons with non-progressive neurological injuries who have the potential to improve voluntary motor control, which mobile BCI-based neurofeedback methods demonstrate or are associated with improved motor outcomes for Neurorehabilitation applications? Methods We searched PubMed, Web of Science, and Scopus databases with all steps from study selection to data extraction performed independently by at least 2 individuals. Search terms included: brain machine or computer interfaces, neurofeedback and motor; however, only studies requiring a motor attempt, versus motor imagery, were retained. Data extraction included participant characteristics, study design details and motor outcomes. Results From 5109 papers, 139 full texts were reviewed with 23 unique studies identified. All utilized EEG and, except for one, were on the stroke population. The most commonly reported functional outcomes were the Fugl-Meyer Assessment (FMA; n = 13) and the Action Research Arm Test (ARAT; n = 6) which were then utilized to assess effectiveness, evaluate design features, and correlate with training doses. Statistically and functionally significant pre-to post training changes were seen in FMA, but not ARAT. Results did not differ between robotic and electrical stimulation feedback paradigms. Notably, FMA outcomes were positively correlated with training dose. Conclusion This review on BCI-based neurofeedback training confirms previous findings of effectiveness in improving motor outcomes with some evidence of enhanced neuroplasticity in adults with stroke. Associative learning paradigms have emerged more recently which may be particularly feasible and effective methods for Neurorehabilitation. More clinical trials in pediatric and adult neurorehabilitation to refine methods and doses and to compare to other evidence-based training strategies are warranted.
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Affiliation(s)
- Ahad Behboodi
- Rehabilitation Medicine Department, National Institutes of Health, Bethesda, MD, USA
| | - Walker A Lee
- Rehabilitation Medicine Department, National Institutes of Health, Bethesda, MD, USA
| | | | - Diane L Damiano
- Rehabilitation Medicine Department, National Institutes of Health, Bethesda, MD, USA.
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6
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Alashram AR, Padua E, Annino G. Effects of Brain-Computer Interface Controlled Functional Electrical Stimulation on Motor Recovery in Stroke Survivors: a Systematic Review. CURRENT PHYSICAL MEDICINE AND REHABILITATION REPORTS 2022. [DOI: 10.1007/s40141-022-00369-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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7
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Remsik AB, van Kan PLE, Gloe S, Gjini K, Williams L, Nair V, Caldera K, Williams JC, Prabhakaran V. BCI-FES With Multimodal Feedback for Motor Recovery Poststroke. Front Hum Neurosci 2022; 16:725715. [PMID: 35874158 PMCID: PMC9296822 DOI: 10.3389/fnhum.2022.725715] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 05/26/2022] [Indexed: 01/31/2023] Open
Abstract
An increasing number of research teams are investigating the efficacy of brain-computer interface (BCI)-mediated interventions for promoting motor recovery following stroke. A growing body of evidence suggests that of the various BCI designs, most effective are those that deliver functional electrical stimulation (FES) of upper extremity (UE) muscles contingent on movement intent. More specifically, BCI-FES interventions utilize algorithms that isolate motor signals-user-generated intent-to-move neural activity recorded from cerebral cortical motor areas-to drive electrical stimulation of individual muscles or muscle synergies. BCI-FES interventions aim to recover sensorimotor function of an impaired extremity by facilitating and/or inducing long-term motor learning-related neuroplastic changes in appropriate control circuitry. We developed a non-invasive, electroencephalogram (EEG)-based BCI-FES system that delivers closed-loop neural activity-triggered electrical stimulation of targeted distal muscles while providing the user with multimodal sensory feedback. This BCI-FES system consists of three components: (1) EEG acquisition and signal processing to extract real-time volitional and task-dependent neural command signals from cerebral cortical motor areas, (2) FES of muscles of the impaired hand contingent on the motor cortical neural command signals, and (3) multimodal sensory feedback associated with performance of the behavioral task, including visual information, linked activation of somatosensory afferents through intact sensorimotor circuits, and electro-tactile stimulation of the tongue. In this report, we describe device parameters and intervention protocols of our BCI-FES system which, combined with standard physical rehabilitation approaches, has proven efficacious in treating UE motor impairment in stroke survivors, regardless of level of impairment and chronicity.
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Affiliation(s)
- Alexander B. Remsik
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- School of Medicine and Public Health, Institute for Clinical and Translational Research, University of Wisconsin–Madison, Madison, WI, United States
- Department of Kinesiology, University of Wisconsin–Madison, Madison, WI, United States
| | - Peter L. E. van Kan
- Department of Kinesiology, University of Wisconsin–Madison, Madison, WI, United States
- Neuroscience Training Program, University of Wisconsin–Madison, Madison, WI, United States
| | - Shawna Gloe
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
| | - Klevest Gjini
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Department of Neurology, University of Wisconsin–Madison, Madison, WI, United States
| | - Leroy Williams
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Department of Educational Psychology, University of Wisconsin–Madison, Madison, WI, United States
| | - Veena Nair
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
| | - Kristin Caldera
- Department of Orthopedics and Rehabilitation, School of Medicine and Public Health, University of Wisconsin–Madison, Madison, WI, United States
| | - Justin C. Williams
- Department of Biomedical Engineering, University of Wisconsin–Madison, Madison, WI, United States
- Department of Neurological Surgery, School of Medicine and Public Health, University of Wisconsin–Madison, Madison, WI, United States
| | - Vivek Prabhakaran
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Neuroscience Training Program, University of Wisconsin–Madison, Madison, WI, United States
- Department of Neurology, University of Wisconsin–Madison, Madison, WI, United States
- Department of Psychiatry, School of Medicine and Public Health, University of Wisconsin–Madison, Madison, WI, United States
- Medical Scientist Training Program, School of Medicine and Public Health, University of Wisconsin–Madison, Madison, WI, United States
- Department of Psychology, University of Wisconsin–Madison, Madison, WI, United States
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8
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Huggins JE, Krusienski D, Vansteensel MJ, Valeriani D, Thelen A, Stavisky S, Norton JJS, Nijholt A, Müller-Putz G, Kosmyna N, Korczowski L, Kapeller C, Herff C, Halder S, Guger C, Grosse-Wentrup M, Gaunt R, Dusang AN, Clisson P, Chavarriaga R, Anderson CW, Allison BZ, Aksenova T, Aarnoutse E. Workshops of the Eighth International Brain-Computer Interface Meeting: BCIs: The Next Frontier. BRAIN-COMPUTER INTERFACES 2022; 9:69-101. [PMID: 36908334 PMCID: PMC9997957 DOI: 10.1080/2326263x.2021.2009654] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 11/15/2021] [Indexed: 12/11/2022]
Abstract
The Eighth International Brain-Computer Interface (BCI) Meeting was held June 7-9th, 2021 in a virtual format. The conference continued the BCI Meeting series' interactive nature with 21 workshops covering topics in BCI (also called brain-machine interface) research. As in the past, workshops covered the breadth of topics in BCI. Some workshops provided detailed examinations of specific methods, hardware, or processes. Others focused on specific BCI applications or user groups. Several workshops continued consensus building efforts designed to create BCI standards and increase the ease of comparisons between studies and the potential for meta-analysis and large multi-site clinical trials. Ethical and translational considerations were both the primary topic for some workshops or an important secondary consideration for others. The range of BCI applications continues to expand, with more workshops focusing on approaches that can extend beyond the needs of those with physical impairments. This paper summarizes each workshop, provides background information and references for further study, presents an overview of the discussion topics, and describes the conclusion, challenges, or initiatives that resulted from the interactions and discussion at the workshop.
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Affiliation(s)
- Jane E Huggins
- Department of Physical Medicine and Rehabilitation, Department of Biomedical Engineering, Neuroscience Graduate Program, University of Michigan, Ann Arbor, Michigan, United States 325 East Eisenhower, Room 3017; Ann Arbor, Michigan 48108-5744, 734-936-7177
| | - Dean Krusienski
- Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, VA 23219
| | - Mariska J Vansteensel
- UMC Utrecht Brain Center, Dept of Neurosurgery, University Medical Center Utrecht, The Netherlands
| | | | - Antonia Thelen
- eemagine Medical Imaging Solutions GmbH, Berlin, Germany
| | | | - James J S Norton
- National Center for Adaptive Neurotechnologies, US Department of Veterans Affairs, 113 Holland Ave, Albany, NY 12208
| | - Anton Nijholt
- Faculty EEMCS, University of Twente, Enschede, The Netherlands
| | - Gernot Müller-Putz
- Institute of Neural Engineering, GrazBCI Lab, Graz University of Technology, Stremayrgasse 16/4, 8010 Graz, Austria
| | - Nataliya Kosmyna
- Massachusetts Institute of Technology (MIT), Media Lab, E14-548, Cambridge, MA 02139, Unites States
| | | | | | - Christian Herff
- School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | | | - Christoph Guger
- g.tec medical engineering GmbH/Guger Technologies OG, Austria, Sierningstrasse 14, 4521 Schiedlberg, Austria, +43725122240-0
| | - Moritz Grosse-Wentrup
- Research Group Neuroinformatics, Faculty of Computer Science, Vienna Cognitive Science Hub, Data Science @ Uni Vienna University of Vienna
| | - Robert Gaunt
- Rehab Neural Engineering Labs, Department of Physical Medicine and Rehabilitation, Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA, 3520 5th Ave, Suite 300, Pittsburgh, PA 15213, 412-383-1426
| | - Aliceson Nicole Dusang
- Department of Electrical and Computer Engineering, School of Engineering, Brown University, Carney Institute for Brain Science, Brown University, Providence, RI
- Department of Veterans Affairs Medical Center, Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence, RI
- Center for Neurotechnology and Neurorecovery, Neurology, Massachusetts General Hospital, Boston, MA
| | | | - Ricardo Chavarriaga
- IEEE Standards Association Industry Connections group on neurotechnologies for brain-machine interface, Center for Artificial Intelligence, School of Engineering, ZHAW-Zurich University of Applied Sciences, Switzerland, Switzerland
| | - Charles W Anderson
- Department of Computer Science, Molecular, Cellular and Integrative Neurosience Program, Colorado State University, Fort Collins, CO 80523
| | - Brendan Z Allison
- Dept. of Cognitive Science, Mail Code 0515, University of California at San Diego, La Jolla, United States, 619-534-9754
| | - Tetiana Aksenova
- University Grenoble Alpes, CEA, LETI, Clinatec, Grenoble 38000, France
| | - Erik Aarnoutse
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
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9
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Remsik AB, Gjini K, Williams L, van Kan PLE, Gloe S, Bjorklund E, Rivera CA, Romero S, Young BM, Nair VA, Caldera KE, Williams JC, Prabhakaran V. Ipsilesional Mu Rhythm Desynchronization Correlates With Improvements in Affected Hand Grip Strength and Functional Connectivity in Sensorimotor Cortices Following BCI-FES Intervention for Upper Extremity in Stroke Survivors. Front Hum Neurosci 2021; 15:725645. [PMID: 34776902 PMCID: PMC8581197 DOI: 10.3389/fnhum.2021.725645] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 10/01/2021] [Indexed: 12/13/2022] Open
Abstract
Stroke is a leading cause of acquired long-term upper extremity motor disability. Current standard of care trajectories fail to deliver sufficient motor rehabilitation to stroke survivors. Recent research suggests that use of brain-computer interface (BCI) devices improves motor function in stroke survivors, regardless of stroke severity and chronicity, and may induce and/or facilitate neuroplastic changes associated with motor rehabilitation. The present sub analyses of ongoing crossover-controlled trial NCT02098265 examine first whether, during movements of the affected hand compared to rest, ipsilesional Mu rhythm desynchronization of cerebral cortical sensorimotor areas [Brodmann’s areas (BA) 1-7] is localized and tracks with changes in grip force strength. Secondly, we test the hypothesis that BCI intervention results in changes in frequency-specific directional flow of information transmission (direct path functional connectivity) in BA 1-7 by measuring changes in isolated effective coherence (iCoh) between cerebral cortical sensorimotor areas thought to relate to electrophysiological signatures of motor actions and motor learning. A sample of 16 stroke survivors with right hemisphere lesions (left hand motor impairment), received a maximum of 18–30 h of BCI intervention. Electroencephalograms were recorded during intervention sessions while outcome measures of motor function and capacity were assessed at baseline and completion of intervention. Greater desynchronization of Mu rhythm, during movements of the impaired hand compared to rest, were primarily localized to ipsilesional sensorimotor cortices (BA 1-7). In addition, increased Mu desynchronization in the ipsilesional primary motor cortex, Post vs. Pre BCI intervention, correlated significantly with improvements in hand function as assessed by grip force measurements. Moreover, the results show a significant change in the direction of causal information flow, as measured by iCoh, toward the ipsilesional motor (BA 4) and ipsilesional premotor cortices (BA 6) during BCI intervention. Significant iCoh increases from ipsilesional BA 4 to ipsilesional BA 6 were observed in both Mu [8–12 Hz] and Beta [18–26 Hz] frequency ranges. In summary, the present results are indicative of improvements in motor capacity and behavior, and they are consistent with the view that BCI-FES intervention improves functional motor capacity of the ipsilesional hemisphere and the impaired hand.
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Affiliation(s)
- Alexander B Remsik
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Kinesiology, University of Wisconsin-Madison, Madison, WI, United States.,Institute for Clinical and Translational Research, University of Wisconsin-Madison, Madison, WI, United States
| | - Klevest Gjini
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States
| | - Leroy Williams
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Educational Psychology, University of Wisconsin-Madison, Madison, WI, United States.,Center for Women's Health Research, University of Wisconsin-Madison, Madison, WI, United States
| | - Peter L E van Kan
- Department of Kinesiology, University of Wisconsin-Madison, Madison, WI, United States.,Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Shawna Gloe
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Erik Bjorklund
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States.,Clinical Neuroengineering Training Program, University of Wisconsin-Madison, Madison, WI, United States
| | - Cameron A Rivera
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Sophia Romero
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Brittany M Young
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Kinesiology, University of Wisconsin-Madison, Madison, WI, United States.,Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States.,Clinical Neuroengineering Training Program, University of Wisconsin-Madison, Madison, WI, United States.,Medical Scientist Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Veena A Nair
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Kristin E Caldera
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Justin C Williams
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States.,Department of Neurological Surgery, University of Wisconsin-Madison, Madison, WI, United States
| | - Vivek Prabhakaran
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States.,Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States.,Medical Scientist Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States.,Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States.,Department of Psychology, University of Wisconsin-Madison, Madison, WI, United States
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10
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Abstract
BACKGROUND AND PURPOSE The ARAT (Action Research Arm Test) has been used to classify upper limb motor outcome after stroke in 1 of 3, 4, or 5 categories. The coronavirus disease 2019 (COVID-19) pandemic has encouraged the development of assessments that can be performed quickly and remotely. The aim of this study was to derive and internally validate decision trees for categorizing upper limb motor outcomes at the late subacute and chronic stages of stroke using a subset of ARAT tasks. METHODS This study retrospectively analyzed ARAT scores obtained in-person at 3 months poststroke from 333 patients. In-person ARAT scores were used to categorize patients' 3-month upper limb outcome using classification systems with 3, 4, and 5 outcome categories. Individual task scores from in-person assessments were then used in classification and regression tree analyses to determine subsets of tasks that could accurately categorize upper limb outcome for each of the 3 classification systems. The decision trees developed using 3-month ARAT data were also applied to in-person ARAT data obtained from 157 patients at 6 months poststroke. RESULTS The classification and regression tree analyses produced decision trees requiring 2 to 4 ARAT tasks. The overall accuracy of the cross-validated decision trees ranged from 87.7% (SE, 1.0%) to 96.7% (SE, 2.0%). Accuracy was highest when classifying patients into one of 3 outcome categories and lowest for 5 categories. The decision trees are referred to as FOCUS (Fast Outcome Categorization of the Upper Limb After Stroke) assessments and they remained accurate for 6-month poststroke ARAT scores (overall accuracy range 83.4%-91.7%). CONCLUSIONS A subset of ARAT tasks can accurately categorize upper limb motor outcomes after stroke. Future studies could investigate the feasibility and accuracy of categorizing outcomes using the FOCUS assessments remotely via video call.
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Affiliation(s)
- Harry T Jordan
- Clinical Neuroscience Laboratory, Department of Medicine, The University of Auckland, New Zealand (H.T.J., J.C., C.M.S.)
| | - Joia Che
- Clinical Neuroscience Laboratory, Department of Medicine, The University of Auckland, New Zealand (H.T.J., J.C., C.M.S.).,School of Medicine, Monash University, Melbourne, Australia (J.C.)
| | - Winston D Byblow
- Movement Neuroscience Laboratory, Department of Exercise Sciences, The University of Auckland, New Zealand. (W.D.B.).,Centre for Brain Research, The University of Auckland, New Zealand. (W.D.B., C.M.S.)
| | - Cathy M Stinear
- Clinical Neuroscience Laboratory, Department of Medicine, The University of Auckland, New Zealand (H.T.J., J.C., C.M.S.).,Centre for Brain Research, The University of Auckland, New Zealand. (W.D.B., C.M.S.)
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11
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Ambrosini E, Gasperini G, Zajc J, Immick N, Augsten A, Rossini M, Ballarati R, Russold M, Ferrante S, Ferrigno G, Bulgheroni M, Baccinelli W, Schauer T, Wiesener C, Gfoehler M, Puchinger M, Weber M, Weber S, Pedrocchi A, Molteni F, Krakow K. A Robotic System with EMG-Triggered Functional Eletrical Stimulation for Restoring Arm Functions in Stroke Survivors. Neurorehabil Neural Repair 2021; 35:334-345. [PMID: 33655789 DOI: 10.1177/1545968321997769] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Robotic systems combined with Functional Electrical Stimulation (FES) showed promising results on upper-limb motor recovery after stroke, but adequately-sized randomized controlled trials (RCTs) are still missing. OBJECTIVE To evaluate whether arm training supported by RETRAINER, a passive exoskeleton integrated with electromyograph-triggered functional electrical stimulation, is superior to advanced conventional therapy (ACT) of equal intensity in the recovery of arm functions, dexterity, strength, activities of daily living, and quality of life after stroke. METHODS A single-blind RCT recruiting 72 patients was conducted. Patients, randomly allocated to 2 groups, were trained for 9 weeks, 3 times per week: the experimental group performed task-oriented exercises assisted by RETRAINER for 30 minutes plus ACT (60 minutes), whereas the control group performed only ACT (90 minutes). Patients were assessed before, soon after, and 1 month after the end of the intervention. Outcome measures were as follows: Action Research Arm Test (ARAT), Motricity Index, Motor Activity Log, Box and Blocks Test (BBT), Stroke Specific Quality of Life Scale (SSQoL), and Muscle Research Council. RESULTS All outcomes but SSQoL significantly improved over time in both groups (P < .001); a significant interaction effect in favor of the experimental group was found for ARAT and BBT. ARAT showed a between-group change of 11.5 points (P = .010) at the end of the intervention, which increased to 13.6 points 1 month after. Patients considered RETRAINER moderately usable (System Usability Score of 61.5 ± 22.8). CONCLUSIONS Hybrid robotic systems, allowing to perform personalized, intensive, and task-oriented training, with an enriched sensory feedback, was superior to ACT in improving arm functions and dexterity after stroke.
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Affiliation(s)
| | | | | | - Nancy Immick
- Asklepios Neurologische Klinik Falkenstein, Königstein, Germany
| | - Andreas Augsten
- Asklepios Neurologische Klinik Falkenstein, Königstein, Germany
| | - Mauro Rossini
- Villa Beretta Rehabilitation Center, Costamasnaga, Italy
| | | | | | | | | | | | | | | | | | | | | | | | | | | | - Franco Molteni
- Villa Beretta Rehabilitation Center, Costamasnaga, Italy
| | - Karsten Krakow
- Asklepios Neurologische Klinik Falkenstein, Königstein, Germany
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12
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Krueger J, Reichert C, Dürschmid S, Krauth R, Vogt S, Huchtemann T, Lindquist S, Lamprecht J, Sailer M, Heinze HJ, Hinrichs H, Sweeney-Reed CM. Rehabilitation nach Schlaganfall: Durch Gehirn-Computer-Schnittstelle
vermittelte funktionelle Elektrostimulation. KLIN NEUROPHYSIOL 2020. [DOI: 10.1055/a-1205-7467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
ZusammenfassungEine Gehirn-Computer-Schnittstelle (BCI) in der Rehabilitation von
Schlaganfallpatienten ermöglicht die Steuerung einer funktionellen
Elektrostimulation (FES), um eine Muskelkontraktion in der gelähmten
Extremität zum Zeitpunkt der Bewegungsintention durch Erkennung
entsprechender Hirnsignale auszulösen. Es wird angenommen, dass eine
genaue zeitliche Kohärenz zwischen Bewegungsintention und visuellem
sowie propriozeptivem Feedback, ausgelöst durch eine reale Bewegung,
neuroplastische Prozesse begünstigen und eine funktionelle
Verbesserung der Parese bewirken kann. In dieser systematischen
Übersichtsarbeit zu randomisierten kontrollierten Studien wurden die
Datenbanken Pubmed, Scopus und Web of Science durchsucht und von 516
berücksichtigten Publikationen 13 ausgewählt, die auf 7
Studienpopulationen basierten. Ein direkter Vergleich der Studien ist durch
Unterschiede im Studiendesign erschwert. Fünf Studien berichten von
einer verbesserten motorischen Funktion in der BCI-FES-Gruppe, davon zeigen
3 signifikante Unterschiede zwischen der BCI-FES- und der
Kontrollgruppe.
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Affiliation(s)
- Johanna Krueger
- Neurokybernetik und Rehabilitation, Klinik für Neurologie,
Otto-von-Guericke Universität, Magdeburg
- Krankenhaus Barmherziger Brüder Regensburg
| | - Christoph Reichert
- Abteilung Verhaltensneurologie, Leibniz Institut für
Neurobiologie (LIN), Magdeburg
| | - Stefan Dürschmid
- Abteilung Verhaltensneurologie, Leibniz Institut für
Neurobiologie (LIN), Magdeburg
| | - Richard Krauth
- Neurokybernetik und Rehabilitation, Klinik für Neurologie,
Otto-von-Guericke Universität, Magdeburg
| | - Susanne Vogt
- Klinik für Neurologie, Otto-von-Guericke Universität,
Magdeburg
| | | | | | - Juliane Lamprecht
- MEDIAN Klinik NRZ Magdeburg, MEDIAN Klinik Flechtingen
- An-Institut für Neurorehabilitation, Otto-von-Guericke
Universität, Magdeburg
| | - Michael Sailer
- MEDIAN Klinik NRZ Magdeburg, MEDIAN Klinik Flechtingen
- An-Institut für Neurorehabilitation, Otto-von-Guericke
Universität, Magdeburg
| | - Hans-Jochen Heinze
- Abteilung Verhaltensneurologie, Leibniz Institut für
Neurobiologie (LIN), Magdeburg
- Klinik für Neurologie, Otto-von-Guericke Universität,
Magdeburg
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE),
Magdeburg
| | - Hermann Hinrichs
- Abteilung Verhaltensneurologie, Leibniz Institut für
Neurobiologie (LIN), Magdeburg
- Klinik für Neurologie, Otto-von-Guericke Universität,
Magdeburg
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE),
Magdeburg
- Center for Behavioral Brain Sciences (CBBS), Magdeburg
- Forschungscampus STIMULATE, Magdeburg
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13
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14
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Bai Z, Fong KNK, Zhang JJ, Chan J, Ting KH. Immediate and long-term effects of BCI-based rehabilitation of the upper extremity after stroke: a systematic review and meta-analysis. J Neuroeng Rehabil 2020; 17:57. [PMID: 32334608 PMCID: PMC7183617 DOI: 10.1186/s12984-020-00686-2] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 04/07/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A substantial number of clinical studies have demonstrated the functional recovery induced by the use of brain-computer interface (BCI) technology in patients after stroke. The objective of this review is to evaluate the effect sizes of clinical studies investigating the use of BCIs in restoring upper extremity function after stroke and the potentiating effect of transcranial direct current stimulation (tDCS) on BCI training for motor recovery. METHODS The databases (PubMed, Medline, EMBASE, CINAHL, CENTRAL, PsycINFO, and PEDro) were systematically searched for eligible single-group or clinical controlled studies regarding the effects of BCIs in hemiparetic upper extremity recovery after stroke. Single-group studies were qualitatively described, but only controlled-trial studies were included in the meta-analysis. The PEDro scale was used to assess the methodological quality of the controlled studies. A meta-analysis of upper extremity function was performed by pooling the standardized mean difference (SMD). Subgroup meta-analyses regarding the use of external devices in combination with the application of BCIs were also carried out. We summarized the neural mechanism of the use of BCIs on stroke. RESULTS A total of 1015 records were screened. Eighteen single-group studies and 15 controlled studies were included. The studies showed that BCIs seem to be safe for patients with stroke. The single-group studies consistently showed a trend that suggested BCIs were effective in improving upper extremity function. The meta-analysis (of 12 studies) showed a medium effect size favoring BCIs for improving upper extremity function after intervention (SMD = 0.42; 95% CI = 0.18-0.66; I2 = 48%; P < 0.001; fixed-effects model), while the long-term effect (five studies) was not significant (SMD = 0.12; 95% CI = - 0.28 - 0.52; I2 = 0%; P = 0.540; fixed-effects model). A subgroup meta-analysis indicated that using functional electrical stimulation as the external device in BCI training was more effective than using other devices (P = 0.010). Using movement attempts as the trigger task in BCI training appears to be more effective than using motor imagery (P = 0.070). The use of tDCS (two studies) could not further facilitate the effects of BCI training to restore upper extremity motor function (SMD = - 0.30; 95% CI = - 0.96 - 0.36; I2 = 0%; P = 0.370; fixed-effects model). CONCLUSION The use of BCIs has significant immediate effects on the improvement of hemiparetic upper extremity function in patients after stroke, but the limited number of studies does not support its long-term effects. BCIs combined with functional electrical stimulation may be a better combination for functional recovery than other kinds of neural feedback. The mechanism for functional recovery may be attributed to the activation of the ipsilesional premotor and sensorimotor cortical network.
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Affiliation(s)
- Zhongfei Bai
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR.,Department of Occupational Therapy, Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Shanghai, China.,Department of Rehabilitation Sciences, Tongji University School of Medicine, Shanghai, China
| | - Kenneth N K Fong
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR.
| | - Jack Jiaqi Zhang
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR
| | - Josephine Chan
- School of Occupational Therapy, Institute of Health Sciences, Texas Woman's University, Houston Center, USA
| | - K H Ting
- University Research Facility in Behavioral and Systems Neuroscience, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR
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15
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Zhuang M, Wu Q, Wan F, Hu Y. State-of-the-art non-invasive brain–computer interface for neural rehabilitation: A review. JOURNAL OF NEURORESTORATOLOGY 2020. [DOI: 10.26599/jnr.2020.9040001] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
Brain–computer interface (BCI) is a novel communication method between brain and machine. It enables signals from the human brain to influence or control external devices. Currently, much research interest is focused on the BCI-based neural rehabilitation of patients with motor and cognitive diseases. Over the decades, BCI has become an alternative treatment for motor and cognitive rehabilitation. Previous studies demonstrated the usefulness of BCI intervention in restoring motor function and recovery of the damaged brain. Electroencephalogram (EEG)-based BCI intervention could cast light on the mechanisms underlying neuroplasticity during upper limb recovery by providing feedback to the damaged brain. BCI could act as a useful tool to aid patients with daily communication and basic movement in severe motor loss cases like amyotrophic lateral sclerosis (ALS). Furthermore, recent findings have reported the therapeutic efficacy of BCI in people suffering from other diseases with different levels of motor impairment such as spastic cerebral palsy, neuropathic pain, etc. Besides motor functional recovery, BCI also plays its role in improving the behavior of patients with cognitive diseases like attention-deficit/hyperactivity disorder (ADHD). The BCI-based neurofeedback training is focused on either reducing the ratio of theta and beta rhythm, or enabling the patients to regulate their own slow cortical potentials, and both have made progress in increasing attention and alertness. With summary of several clinical studies with strong evidence, we present cutting edge results from the clinical application of BCI in motor and cognitive diseases, including stroke, spinal cord injury, ALS, and ADHD.
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16
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Abstract
In the past 10 years, brain-computer interfaces (BCIs) for controlling assistive devices have seen tremendous progress with respect to reliability and learnability, and numerous exemplary applications were demonstrated to be controllable by a BCI. Yet, BCI-controlled applications are hardly used for patients with neurologic or neurodegenerative disease. Such patient groups are considered potential end-users of BCI, specifically for replacing or improving lost function. We argue that BCI research and development still faces a translational gap, i.e., the knowledge of how to bring BCIs from the laboratory to the field is insufficient. BCI-controlled applications lack usability and accessibility; both constitute two sides of one coin, which is the key to use in daily life and to prevent nonuse. To increase usability, we suggest rigorously adopting the user-centered design in applied BCI research and development. To provide accessibility, assistive technology (AT) experts, providers, and other stakeholders have to be included in the user-centered process. BCI experts have to ensure the transfer of knowledge to AT professionals, and listen to the needs of primary, secondary, and tertiary end-users of BCI technology. Addressing both, usability and accessibility, in applied BCI research and development will bridge the translational gap and ensure that the needs of clinical end-users are heard, understood, addressed, and fulfilled.
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Affiliation(s)
- Andrea Kübler
- Institute of Psychology, University of Würzburg, Würzburg, Germany
| | - Femke Nijboer
- Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, The Netherlands
| | - Sonja Kleih
- Institute of Psychology, University of Würzburg, Würzburg, Germany
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17
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Remsik AB, Williams L, Gjini K, Dodd K, Thoma J, Jacobson T, Walczak M, McMillan M, Rajan S, Young BM, Nigogosyan Z, Advani H, Mohanty R, Tellapragada N, Allen J, Mazrooyisebdani M, Walton LM, van Kan PLE, Kang TJ, Sattin JA, Nair VA, Edwards DF, Williams JC, Prabhakaran V. Ipsilesional Mu Rhythm Desynchronization and Changes in Motor Behavior Following Post Stroke BCI Intervention for Motor Rehabilitation. Front Neurosci 2019; 13:53. [PMID: 30899211 PMCID: PMC6417367 DOI: 10.3389/fnins.2019.00053] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Accepted: 01/21/2019] [Indexed: 01/26/2023] Open
Abstract
Loss of motor function is a common deficit following stroke insult and often manifests as persistent upper extremity (UE) disability which can affect a survivor's ability to participate in activities of daily living. Recent research suggests the use of brain-computer interface (BCI) devices might improve UE function in stroke survivors at various times since stroke. This randomized crossover-controlled trial examines whether intervention with this BCI device design attenuates the effects of hemiparesis, encourages reorganization of motor related brain signals (EEG measured sensorimotor rhythm desynchronization), and improves movement, as measured by the Action Research Arm Test (ARAT). A sample of 21 stroke survivors, presenting with varied times since stroke and levels of UE impairment, received a maximum of 18-30 h of intervention with a novel electroencephalogram-based BCI-driven functional electrical stimulator (EEG-BCI-FES) device. Driven by spectral power recordings from contralateral EEG electrodes during cued attempted grasping of the hand, the user's input to the EEG-BCI-FES device modulates horizontal movement of a virtual cursor and also facilitates concurrent stimulation of the impaired UE. Outcome measures of function and capacity were assessed at baseline, mid-therapy, and at completion of therapy while EEG was recorded only during intervention sessions. A significant increase in r-squared values [reflecting Mu rhythm (8-12 Hz) desynchronization as the result of attempted movements of the impaired hand] presented post-therapy compared to baseline. These findings suggest that intervention corresponds with greater desynchronization of Mu rhythm in the ipsilesional hemisphere during attempted movements of the impaired hand and this change is related to changes in behavior as a result of the intervention. BCI intervention may be an effective way of addressing the recovery of a stroke impaired UE and studying neuromechanical coupling with motor outputs. Clinical Trial Registration: ClinicalTrials.gov, identifier NCT02098265.
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Affiliation(s)
- Alexander B. Remsik
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Department of Kinesiology, University of Wisconsin–Madison, Madison, WI, United States
- Institute for Clinical and Translational Research, University of Wisconsin–Madison, Madison, WI, United States
| | - Leroy Williams
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Department of Educational Psychology, University of Wisconsin–Madison, Madison, WI, United States
- Center for Women’s Health Research, University of Wisconsin–Madison, Madison, WI, United States
| | - Klevest Gjini
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Department of Neurology, University of Wisconsin–Madison, Madison, WI, United States
| | - Keith Dodd
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Department of Biomedical Engineering, University of Wisconsin–Madison, Madison, WI, United States
| | - Jaclyn Thoma
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Tyler Jacobson
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Matt Walczak
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
| | - Matthew McMillan
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Department of Biomedical Engineering, University of Wisconsin–Madison, Madison, WI, United States
| | - Shruti Rajan
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Department of Psychology, University of Wisconsin–Madison, Madison, WI, United States
| | - Brittany M. Young
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Institute for Clinical and Translational Research, University of Wisconsin–Madison, Madison, WI, United States
- Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
- Clinical Neuroengineering Training Program, University of Wisconsin–Madison, Madison, WI, United States
- Medical Scientist Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Zack Nigogosyan
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
| | - Hemali Advani
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
| | - Rosaleena Mohanty
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Department of Electrical and Computer Engineering, University of Wisconsin–Madison, Madison, WI, United States
| | - Neelima Tellapragada
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
| | - Janerra Allen
- Department of Materials Science and Engineering, University of Wisconsin–Madison, Madison, WI, United States
| | | | - Leo M. Walton
- Department of Biomedical Engineering, University of Wisconsin–Madison, Madison, WI, United States
- Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Peter L. E. van Kan
- Department of Kinesiology, University of Wisconsin–Madison, Madison, WI, United States
| | - Theresa J. Kang
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Department of Neurology, University of Wisconsin–Madison, Madison, WI, United States
| | - Justin A. Sattin
- Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Veena A. Nair
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
| | | | - Justin C. Williams
- Department of Biomedical Engineering, University of Wisconsin–Madison, Madison, WI, United States
- Department of Neurological Surgery, University of Wisconsin–Madison, Madison, WI, United States
| | - Vivek Prabhakaran
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Department of Neurology, University of Wisconsin–Madison, Madison, WI, United States
- Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
- Department of Psychology, University of Wisconsin–Madison, Madison, WI, United States
- Medical Scientist Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
- Department of Psychiatry, University of Wisconsin–Madison, Madison, WI, United States
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