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Rong F, Yang B, Guan C. Decoding Multi-Class Motor Imagery From Unilateral Limbs Using EEG Signals. IEEE Trans Neural Syst Rehabil Eng 2024; 32:3399-3409. [PMID: 39236133 DOI: 10.1109/tnsre.2024.3454088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/07/2024]
Abstract
The EEG is a widely utilized neural signal source, particularly in motor imagery-based brain-computer interface (MI-BCI), offering distinct advantages in applications like stroke rehabilitation. Current research predominantly concentrates on the bilateral limbs paradigm and decoding, but the use scenarios for stroke rehabilitation are typically for unilateral upper limbs. There is a significant challenge to decoding unilateral MI of multitasks due to the overlapped spatial neural activities of the tasks. This study aims to formulate a novel MI-BCI experimental paradigm for unilateral limbs with multitasks. The paradigm encompasses four imagined movement directions: top-bottom, left-right, top right-bottom left, and top left-bottom right. Forty-six healthy subjects participated in this experiment. Commonly used machine learning techniques, such as FBCSP, EEGNet, deepConvNet, and FBCNet, were employed for evaluation. To improve decoding accuracy, we propose an MVCA method that introduces temporal convolution and attention mechanism to effectively capture temporal features from multiple perspectives. With the MVCA model, we have achieved 40.6% and 64.89% classification accuracies for the four-class and two-class scenarios (top right-bottom left and top left-bottom right), respectively. Conclusion: This is the first study demonstrating that motor imagery of multiple directions in unilateral limbs can be decoded. In particular, decoding two directions, right top to left bottom and left top to right bottom, provides the best accuracy, which sheds light on future studies. This study advances the development of the MI-BCI paradigm, offering preliminary evidence for the feasibility of decoding multiple directional information from EEG. This, in turn, enhances the dimensions of MI control commands.
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Drigas A, Sideraki A. Brain Neuroplasticity Leveraging Virtual Reality and Brain-Computer Interface Technologies. SENSORS (BASEL, SWITZERLAND) 2024; 24:5725. [PMID: 39275636 PMCID: PMC11397861 DOI: 10.3390/s24175725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2024] [Revised: 08/09/2024] [Accepted: 08/26/2024] [Indexed: 09/16/2024]
Abstract
This study explores neuroplasticity through the use of virtual reality (VR) and brain-computer interfaces (BCIs). Neuroplasticity is the brain's ability to reorganize itself by forming new neural connections in response to learning, experience, and injury. VR offers a controlled environment to manipulate sensory inputs, while BCIs facilitate real-time monitoring and modulation of neural activity. By combining VR and BCI, researchers can stimulate specific brain regions, trigger neurochemical changes, and influence cognitive functions such as memory, perception, and motor skills. Key findings indicate that VR and BCI interventions are promising for rehabilitation therapies, treatment of phobias and anxiety disorders, and cognitive enhancement. Personalized VR experiences, adapted based on BCI feedback, enhance the efficacy of these interventions. This study underscores the potential for integrating VR and BCI technologies to understand and harness neuroplasticity for cognitive and therapeutic applications. The researchers utilized the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method to conduct a comprehensive and systematic review of the existing literature on neuroplasticity, VR, and BCI. This involved identifying relevant studies through database searches, screening for eligibility, and assessing the quality of the included studies. Data extraction focused on the effects of VR and BCI on neuroplasticity and cognitive functions. The PRISMA method ensured a rigorous and transparent approach to synthesizing evidence, allowing the researchers to draw robust conclusions about the potential of VR and BCI technologies in promoting neuroplasticity and cognitive enhancement.
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Affiliation(s)
- Athanasios Drigas
- Net Media Lab & Mind & Brain R&D, Institute of Informatics & Telecommunications, National Centre of Scientific Research 'Demokritos', 15341 Athens, Greece
| | - Angeliki Sideraki
- Department of Secondary Education, Kapodistrian University of Athens, 15772 Athens, Greece
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Chen C, Song Y, Chen D, Zhu J, Ning H, Xiao R. Design and application of pneumatic rehabilitation glove system based on brain-computer interface. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2024; 95:095108. [PMID: 39248624 DOI: 10.1063/5.0225972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Accepted: 08/22/2024] [Indexed: 09/10/2024]
Abstract
Stroke has been the second leading cause of death and disability worldwide. With the innovation of therapeutic schedules, its death rate has decreased significantly but still guides chronic movement disorders. Due to the lack of independent activities and minimum exercise standards, the traditional rehabilitation means of occupational therapy and constraint-induced movement therapy pose challenges in stroke patients with severe impairments. Therefore, specific and effective rehabilitation methods seek innovation. To address the overlooked limitation, we design a pneumatic rehabilitation glove system. Specially, we developed a pneumatic glove, which utilizes ElectroEncephaloGram (EEG) acquisition to gain the EEG signals. A proposed EEGTran model is inserted into the system to distinguish the specific motor imagination behavior, thus, the glove can perform specific activities according to the patient's imagination, facilitating the patients with severe movement disorders and promoting the rehabilitation technology. The experimental results show that the proposed EEGTrans reached an accuracy of 87.3% and outperformed that of competitors. It demonstrates that our pneumatic rehabilitation glove system contributes to the rehabilitation training of stroke patients.
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Affiliation(s)
- Cheng Chen
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Yize Song
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Duoyou Chen
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Jiahua Zhu
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Huansheng Ning
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Ruoxiu Xiao
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
- Shunde Graduate School of University of Science and Technology Beijing, Foshan 100024, China
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Cioffi E, Hutber A, Molloy R, Murden S, Yurkewich A, Kirton A, Lin JP, Gimeno H, McClelland VM. EEG-based sensorimotor neurofeedback for motor neurorehabilitation in children and adults: A scoping review. Clin Neurophysiol 2024; 167:143-166. [PMID: 39321571 DOI: 10.1016/j.clinph.2024.08.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 07/17/2024] [Accepted: 08/03/2024] [Indexed: 09/27/2024]
Abstract
OBJECTIVE Therapeutic interventions for children and young people with dystonia and dystonic/dyskinetic cerebral palsy are limited. EEG-based neurofeedback is emerging as a neurorehabilitation tool. This scoping review maps research investigating EEG-based sensorimotor neurofeedback in adults and children with neurological motor impairments, including augmentative strategies. METHODS MEDLINE, CINAHL and Web of Science databases were searched up to 2023 for relevant studies. Study selection and data extraction were conducted independently by at least two reviewers. RESULTS Of 4380 identified studies, 133 were included, only three enrolling children. The most common diagnosis was adult-onset stroke (77%). Paradigms mostly involved upper limb motor imagery or motor attempt. Common neurofeedback modes included visual, haptic and/or electrical stimulation. EEG parameters varied widely and were often incompletely described. Two studies applied augmentative strategies. Outcome measures varied widely and included classification accuracy of the Brain-Computer Interface, degree of enhancement of mu rhythm modulation or other neurophysiological parameters, and clinical/motor outcome scores. Few studies investigated whether functional outcomes related specifically to the EEG-based neurofeedback. CONCLUSIONS There is limited evidence exploring EEG-based sensorimotor neurofeedback in individuals with movement disorders, especially in children. Further clarity of neurophysiological parameters is required to develop optimal paradigms for evaluating sensorimotor neurofeedback. SIGNIFICANCE The expanding field of sensorimotor neurofeedback offers exciting potential as a non-invasive therapy. However, this needs to be balanced by robust study design and detailed methodological reporting to ensure reproducibility and validation that clinical improvements relate to induced neurophysiological changes.
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Affiliation(s)
- Elena Cioffi
- Department of Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Department of Paediatric Neurosciences, Evelina London Children's Hospital, London, UK.
| | - Anna Hutber
- Department of Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Department of Paediatric Neurosciences, Evelina London Children's Hospital, London, UK.
| | - Rob Molloy
- Islington Paediatric Occupational Therapy, Whittington Hospital NHS Trust, London, UK; Barts Bone and Joint Health, Blizard Institute, Queen Mary University of London, London, UK.
| | - Sarah Murden
- Department of Paediatric Neurology, King's College Hospital NHS Foundation Trust, London, UK.
| | - Aaron Yurkewich
- Mechatronics Engineering, Ontario Tech University, Ontario, Canada.
| | - Adam Kirton
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
| | - Jean-Pierre Lin
- Department of Paediatric Neurosciences, Evelina London Children's Hospital, London, UK.
| | - Hortensia Gimeno
- Barts Bone and Joint Health, Blizard Institute, Queen Mary University of London, London, UK; The Royal London Hospital and Tower Hamlets Community Children's Therapy Services, Barts Health NHS Trust, London, UK.
| | - Verity M McClelland
- Department of Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Department of Paediatric Neurosciences, Evelina London Children's Hospital, London, UK.
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Krueger J, Krauth R, Reichert C, Perdikis S, Vogt S, Huchtemann T, Dürschmid S, Sickert A, Lamprecht J, Huremovic A, Görtler M, Nasuto SJ, Tsai IC, Knight RT, Hinrichs H, Heinze HJ, Lindquist S, Sailer M, Millán JDR, Sweeney-Reed CM. Hebbian plasticity induced by temporally coincident BCI enhances post-stroke motor recovery. Sci Rep 2024; 14:18700. [PMID: 39134592 PMCID: PMC11319604 DOI: 10.1038/s41598-024-69037-8] [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: 04/14/2023] [Accepted: 07/30/2024] [Indexed: 08/15/2024] Open
Abstract
Functional electrical stimulation (FES) can support functional restoration of a paretic limb post-stroke. Hebbian plasticity depends on temporally coinciding pre- and post-synaptic activity. A tight temporal relationship between motor cortical (MC) activity associated with attempted movement and FES-generated visuo-proprioceptive feedback is hypothesized to enhance motor recovery. Using a brain-computer interface (BCI) to classify MC spectral power in electroencephalographic (EEG) signals to trigger FES-delivery with detection of movement attempts improved motor outcomes in chronic stroke patients. We hypothesized that heightened neural plasticity earlier post-stroke would further enhance corticomuscular functional connectivity and motor recovery. We compared subcortical non-dominant hemisphere stroke patients in BCI-FES and Random-FES (FES temporally independent of MC movement attempt detection) groups. The primary outcome measure was the Fugl-Meyer Assessment, Upper Extremity (FMA-UE). We recorded high-density EEG and transcranial magnetic stimulation-induced motor evoked potentials before and after treatment. The BCI group showed greater: FMA-UE improvement; motor evoked potential amplitude; beta oscillatory power and long-range temporal correlation reduction over contralateral MC; and corticomuscular coherence with contralateral MC. These changes are consistent with enhanced post-stroke motor improvement when movement is synchronized with MC activity reflecting attempted movement.
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Affiliation(s)
- Johanna Krueger
- Neurocybernetics and Rehabilitation, Department of Neurology, Otto von Guericke University, Magdeburg, Germany
| | - Richard Krauth
- Neurocybernetics and Rehabilitation, Department of Neurology, Otto von Guericke University, Magdeburg, Germany
| | | | - Serafeim Perdikis
- School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
| | - Susanne Vogt
- Neurocybernetics and Rehabilitation, Department of Neurology, Otto von Guericke University, Magdeburg, Germany
- Department of Neurology, Otto von Guericke University, Magdeburg, Germany
- Department of Psychosomatic Medicine and Psychotherapy, Otto von Guericke University, Magdeburg, Germany
| | - Tessa Huchtemann
- Neurocybernetics and Rehabilitation, Department of Neurology, Otto von Guericke University, Magdeburg, Germany
- Department of Neurology, Otto von Guericke University, Magdeburg, Germany
- Department of Neurology, University Hospital Münster, Münster, Germany
| | - Stefan Dürschmid
- Leibniz Institute for Neurobiology, Magdeburg, Germany
- Center for Behavioral Brain Sciences (CBBS), Otto von Guericke University, Magdeburg, Germany
| | - Almut Sickert
- Neurorehabilitation Centre, MEDIAN, Magdeburg, Germany
| | - Juliane Lamprecht
- Neurorehabilitation Centre, MEDIAN, Magdeburg, Germany
- Health and Care Sciences, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - Almir Huremovic
- Neurorehabilitation Centre, MEDIAN, Magdeburg, Germany
- Department of Neurology, Ingolstadt Hospital, Ingolstadt, Germany
| | - Michael Görtler
- Department of Neurology, Otto von Guericke University, Magdeburg, Germany
| | | | - I-Chin Tsai
- Neurocybernetics and Rehabilitation, Department of Neurology, Otto von Guericke University, Magdeburg, Germany
| | - Robert T Knight
- Helen Wills Neuroscience Institute, University of California -Berkeley, Berkeley, USA
- Department of Psychology, University of California -Berkeley, Berkeley, USA
| | - Hermann Hinrichs
- Leibniz Institute for Neurobiology, Magdeburg, Germany
- Department of Neurology, Otto von Guericke University, Magdeburg, Germany
- Center for Behavioral Brain Sciences (CBBS), Otto von Guericke University, Magdeburg, Germany
| | - Hans-Jochen Heinze
- Leibniz Institute for Neurobiology, Magdeburg, Germany
- University Hospital Magdeburg, Otto von Guericke University, Magdeburg, Germany
| | - Sabine Lindquist
- Department of Neurology, Pfeiffersche Stiftung, Magdeburg, Germany
| | | | - Jose Del R Millán
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, USA
- Department of Neurology, The University of Texas at Austin, Austin, USA
- Mulva Clinic for the Neurosciences, The University of Texas at Austin, Austin, USA
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, USA
| | - Catherine M Sweeney-Reed
- Neurocybernetics and Rehabilitation, Department of Neurology, Otto von Guericke University, Magdeburg, Germany.
- Center for Behavioral Brain Sciences (CBBS), Otto von Guericke University, Magdeburg, Germany.
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Wang J, Bi L, Fei W, Xu X, Liu A, Mo L, Genetu Feleke A. Neural Correlate and Movement Decoding of Simultaneous-and-Sequential Bimanual Movements Using EEG Signals. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2087-2095. [PMID: 38805337 DOI: 10.1109/tnsre.2024.3406371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/30/2024]
Abstract
Bimanual coordination is important for developing a natural motor brain-computer interface (BCI) from electroencephalogram (EEG) signals, covering the aspects of bilateral arm training for rehabilitation, bimanual coordination for daily-life assistance, and also improving the multidimensional control of BCIs. For the same task targets of both hands, simultaneous and sequential bimanual movements are two different bimanual coordination manners. Planning and performing motor sequences are the fundamental abilities of humans, and it is more natural to execute sequential movements compared to simultaneous movements in many complex tasks. However, to date, for these two different manners in which two hands coordinated to reach the same task targets, the differences in the neural correlate and also the feasibility of movement discrimination have not been explored. In this study, we aimed to investigate these two issues based on a bimanual reaching task for the first time. Finally, neural correlates in the view of the movement-related cortical potentials, event-related oscillations, and source imaging showed unique neural encoding patterns of sequential movements. Besides, for the same task targets of both hands, the simultaneous and sequential bimanual movements were successfully discriminated in both pre-movement and movement execution periods. This study revealed the neural encoding patterns of sequential bimanual movements and presented its values in developing a more natural and good-performance motor BCI.
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Lo YT, Lim MJR, Kok CY, Wang S, Blok SZ, Ang TY, Ng VYP, Rao JP, Chua KSG. Neural Interface-Based Motor Neuroprosthesis in Poststroke Upper Limb Neurorehabilitation: An Individual Patient Data Meta-analysis. Arch Phys Med Rehabil 2024:S0003-9993(24)00910-9. [PMID: 38579958 DOI: 10.1016/j.apmr.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/28/2024] [Accepted: 04/01/2024] [Indexed: 04/07/2024]
Abstract
OBJECTIVE To determine the efficacy of neural interface-based neurorehabilitation, including brain-computer interface, through conventional and individual patient data (IPD) meta-analysis and to assess clinical parameters associated with positive response to neural interface-based neurorehabilitation. DATA SOURCES PubMed, EMBASE, and Cochrane Library databases up to February 2022 were reviewed. STUDY SELECTION Studies using neural interface-controlled physical effectors (functional electrical stimulation and/or powered exoskeletons) and reported Fugl-Meyer Assessment-upper-extremity (FMA-UE) scores were identified. This meta-analysis was prospectively registered on PROSPERO (#CRD42022312428). PRISMA guidelines were followed. DATA EXTRACTION Changes in FMA-UE scores were pooled to estimate the mean effect size. Subgroup analyses were performed on clinical parameters and neural interface parameters with both study-level variables and IPD. DATA SYNTHESIS Forty-six studies containing 617 patients were included. Twenty-nine studies involving 214 patients reported IPD. FMA-UE scores increased by a mean of 5.23 (95% confidence interval [CI]: 3.85-6.61). Systems that used motor attempt resulted in greater FMA-UE gain than motor imagery, as did training lasting >4 vs ≤4 weeks. On IPD analysis, the mean time-to-improvement above minimal clinically important difference (MCID) was 12 weeks (95% CI: 7 to not reached). At 6 months, 58% improved above MCID (95% CI: 41%-70%). Patients with severe impairment (P=.042) and age >50 years (P=.0022) correlated with the failure to improve above the MCID on univariate log-rank tests. However, these factors were only borderline significant on multivariate Cox analysis (hazard ratio [HR] 0.15, P=.08 and HR 0.47, P=.06, respectively). CONCLUSION Neural interface-based motor rehabilitation resulted in significant, although modest, reductions in poststroke impairment and should be considered for wider applications in stroke neurorehabilitation.
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Affiliation(s)
- Yu Tung Lo
- Department of Neurosurgery, National Neuroscience Institute; Duke-NUS Medical School.
| | - Mervyn Jun Rui Lim
- Department of Neurosurgery, National University Hospital; National University of Singapore, Yong Loo Lin School of Medicine
| | - Chun Yen Kok
- Department of Neurosurgery, National Neuroscience Institute
| | - Shilin Wang
- Department of Neurosurgery, National Neuroscience Institute
| | | | - Ting Yao Ang
- Department of Neurosurgery, National Neuroscience Institute
| | | | - Jai Prashanth Rao
- Department of Neurosurgery, National Neuroscience Institute; Duke-NUS Medical School
| | - Karen Sui Geok Chua
- National University of Singapore, Yong Loo Lin School of Medicine; Institute of Rehabilitation Excellence, Tan Tock Seng Hospital Rehabilitation Centre; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
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Sharma VS, Sharath HV, Sasun AR. Effectiveness of Syrebo's Glove Rehabilitation Program in a Patient With Middle Cerebral Artery Infarct: A Case Report. Cureus 2024; 16:e59314. [PMID: 38817453 PMCID: PMC11136872 DOI: 10.7759/cureus.59314] [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: 04/02/2024] [Accepted: 04/29/2024] [Indexed: 06/01/2024] Open
Abstract
In India, stroke is a significant health concern, with an estimated prevalence of around 1.54% in adults over 20 years old. The incidence of stroke in India varies regionally but is generally high due to factors like hypertension and lifestyle changes. Ischemic strokes comprise the majority, particularly in the middle cerebral artery (MCA) territory. MCA stroke presents with diverse symptoms such as weakness, speech difficulties, and vision problems, emphasizing the need for comprehensive rehabilitation. Physiotherapy plays a vital role in addressing these challenges, focusing on strength, coordination, mobility, and independence through tailored interventions. Additionally, soft robotic gloves, such as Syrebo's rehabilitation, offer promising advancements in neurorehabilitation by enhancing motor recovery and functional abilities, particularly in improving grip strength and hand functionality, thus improving outcomes for stroke patients. This case describes a 66-year-old female presenting with sudden left-sided weakness, slurred speech, and facial deviation indicative of bilateral MCA territory infarct. After admission requiring ventilation and medication, imaging confirmed the diagnosis. Following stabilization, she underwent neurophysiotherapy for rehabilitation. Neurological examination revealed deficits in muscle tone, reflexes, cranial nerve function, language, and swallowing. Outcome measures indicated progress in rehabilitation. The case underscores the significance of timely diagnosis and personalized rehabilitation in optimizing outcomes for MCA territory stroke patients.
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Affiliation(s)
- Vaishnavi S Sharma
- Department of Paediatric Physiotherapy, Center for Advanced Physiotherapy Education & Research (CAPER) Ravi Nair Physiotherapy College, Datta Meghe Institute of Higher Education and Research (DU) Sawangi Meghe, Wardha, IND
| | - H V Sharath
- Department of Paediatric Physiotherapy, Center for Advanced Physiotherapy Education & Research (CAPER) Ravi Nair Physiotherapy College, Datta Meghe Institute of Higher Education and Research (DU) Sawangi Meghe, Wardha, IND
| | - Anam R Sasun
- Department of Neuro-Physiotherapy, Center for Advanced Physiotherapy Education & Research (CAPER) Ravi Nair Physiotherapy College, Datta Meghe Institute of Higher Education and Research (DU) Sawangi Meghe, Wardha, IND
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Ma P, Dong C, Lin R, Liu H, Lei D, Chen X, Liu H. A brain functional network feature extraction method based on directed transfer function and graph theory for MI-BCI decoding tasks. Front Neurosci 2024; 18:1306283. [PMID: 38586195 PMCID: PMC10996401 DOI: 10.3389/fnins.2024.1306283] [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: 10/03/2023] [Accepted: 03/08/2024] [Indexed: 04/09/2024] Open
Abstract
Background The development of Brain-Computer Interface (BCI) technology has brought tremendous potential to various fields. In recent years, prominent research has focused on enhancing the accuracy of BCI decoding algorithms by effectively utilizing meaningful features extracted from electroencephalographic (EEG) signals. Objective This paper proposes a method for extracting brain functional network features based on directed transfer function (DTF) and graph theory. The method incorporates the extracted brain network features with common spatial pattern (CSP) to enhance the performance of motor imagery (MI) classification task. Methods The signals from each electrode of the EEG, utilizing a total of 32 channels, are used as input signals for the network nodes. In this study, 26 healthy participants were recruited to provide EEG data. The brain functional network is constructed in Alpha and Beta bands using the DTF method. The node degree (ND), clustering coefficient (CC), and global efficiency (GE) of the brain functional network are obtained using graph theory. The DTF network features and graph theory are combined with the traditional signal processing method, the CSP algorithm. The redundant network features are filtered out using the Lasso method, and finally, the fused features are classified using a support vector machine (SVM), culminating in a novel approach we have termed CDGL. Results For Beta frequency band, with 8 electrodes, the proposed CDGL method achieved an accuracy of 89.13%, a sensitivity of 90.15%, and a specificity of 88.10%, which are 14.10, 16.69, and 11.50% percentage higher than the traditional CSP method (75.03, 73.46, and 76.60%), respectively. Furthermore, the results obtained with 8 channels were superior to those with 4 channels (82.31, 83.35, and 81.74%), and the result for the Beta frequency band were better than those for the Alpha frequency band (87.42, 87.48, and 87.36%). Similar results were also obtained on two public datasets, where the CDGL algorithm's performance was found to be optimal. Conclusion The feature fusion of DTF network and graph theory features enhanced CSP algorithm's performance in MI task classification. Increasing the number of channels allows for more EEG signal feature information, enhancing the model's sensitivity and discriminative ability toward specific activities in brain regions. It should be noted that the functional brain network features in the Beta band exhibit superior performance improvement for the algorithm compared to those in the Alpha band.
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Affiliation(s)
- Pengfei Ma
- College of Electric Power, Inner Mongolia University of Technology, Hohhot, China
- Intelligent Energy Technology and Equipment Engineering Research Centre of Colleges and Universities in Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, China
- College of Computer and Software Engineering, Dalian Neusoft University of Information, Dalian, China
| | - Chaoyi Dong
- College of Electric Power, Inner Mongolia University of Technology, Hohhot, China
- Intelligent Energy Technology and Equipment Engineering Research Centre of Colleges and Universities in Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, China
- Engineering Research Center of Large Energy Storage Technology, Ministry of Education, Hohhot, Inner Mongolia, China
| | - Ruijing Lin
- College of Electric Power, Inner Mongolia University of Technology, Hohhot, China
- Intelligent Energy Technology and Equipment Engineering Research Centre of Colleges and Universities in Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, China
| | - Huanzi Liu
- College of Electric Power, Inner Mongolia University of Technology, Hohhot, China
- Intelligent Energy Technology and Equipment Engineering Research Centre of Colleges and Universities in Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, China
| | - Dongyang Lei
- College of Electric Power, Inner Mongolia University of Technology, Hohhot, China
- Intelligent Energy Technology and Equipment Engineering Research Centre of Colleges and Universities in Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, China
| | - Xiaoyan Chen
- College of Electric Power, Inner Mongolia University of Technology, Hohhot, China
- Intelligent Energy Technology and Equipment Engineering Research Centre of Colleges and Universities in Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, China
- Engineering Research Center of Large Energy Storage Technology, Ministry of Education, Hohhot, Inner Mongolia, China
| | - Huan Liu
- College of Computer and Software Engineering, Dalian Neusoft University of Information, Dalian, China
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Jayavel P, Karthik V, Mathunny JJ, Jothi S, Devaraj A. Hand assistive device with suction cup (HADS) technology for poststroke patients. Proc Inst Mech Eng H 2024; 238:160-169. [PMID: 38189258 DOI: 10.1177/09544119231221190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
A stroke is a neurological disease that primarily causes paralysis. Besides paraplegia, all other types of paralysis affect the upper extremity. Advanced technologies, such as wearable devices and rehabilitation regimens, are also being developed to enhance the functional ability of a stroke person to grasp and release daily living objects. In this research, we developed a rehabilitation functional assist device combining a flexion and extension mechanism with suction cup technology (hybrid technology) to help post-stroke patients improve their hand grip strength in day-to-day grasping activities. Ten poststroke hemiplegia patients were studied to test the functional ability of the impaired hand by wearing and not wearing the device. The outcomes were validated by three standard clinical tests, such as the Toronto Rehabilitation Institute - Hand Functional Test (TRI-HFT), the Chedoke Arm Hand Activity Inventory (CAHAI-9), and the Fugl-Meyer Assessment (FMA) with overall score improvements of 14.5 ± 3.8-25 ± 2.2 (p = 0.005), 5.4 ± 2.8-10 ± 1.6 (p = 0.008), and 9.6 ± 2.6-17 ± 2.4 (p = 0.005) respectively. The p-value for each of the three evaluations was less than 0.05, indicating significantly improved results and the average feedback score of the participants was 3.8 out of 5. The proposed device significantly increased impaired hand functionality in post-stroke patients. The subjects could complete some of the grasping tasks that they could not grasp without the device.Clinical trial registrationThe Clinical Trial Registry of India approved the work CTRI/2022/02/040495 described in this manuscript.
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Affiliation(s)
- Porkodi Jayavel
- Department of Biomedical Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, India
| | - Varshini Karthik
- Department of Biomedical Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, India
| | - Jaison Jacob Mathunny
- Department of Biomedical Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, India
| | - Suresh Jothi
- SRM College of Physiotherapy, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, India
| | - Ashokkumar Devaraj
- Department of Biomedical Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, India
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Oh E, Shin S, Kim SP. Brain-computer interface in critical care and rehabilitation. Acute Crit Care 2024; 39:24-33. [PMID: 38224957 PMCID: PMC11002623 DOI: 10.4266/acc.2023.01382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 11/08/2023] [Indexed: 01/17/2024] Open
Abstract
This comprehensive review explores the broad landscape of brain-computer interface (BCI) technology and its potential use in intensive care units (ICUs), particularly for patients with motor impairments such as quadriplegia or severe brain injury. By employing brain signals from various sensing techniques, BCIs offer enhanced communication and motor rehabilitation strategies for patients. This review underscores the concept and efficacy of noninvasive, electroencephalogram-based BCIs in facilitating both communicative interactions and motor function recovery. Additionally, it highlights the current research gap in intuitive "stop" mechanisms within motor rehabilitation protocols, emphasizing the need for advancements that prioritize patient safety and individualized responsiveness. Furthermore, it advocates for more focused research that considers the unique requirements of ICU environments to address the challenges arising from patient variability, fatigue, and limited applicability of current BCI systems outside of experimental settings.
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Affiliation(s)
- Eunseo Oh
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Korea
| | - Seyoung Shin
- Department of Mechanical Engineering, Sungkyunkwan University, Suwon, Korea
| | - Sung-Phil Kim
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Korea
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Proietti T, Nuckols K, Grupper J, Schwerz de Lucena D, Inirio B, Porazinski K, Wagner D, Cole T, Glover C, Mendelowitz S, Herman M, Breen J, Lin D, Walsh C. Combining soft robotics and telerehabilitation for improving motor function after stroke. WEARABLE TECHNOLOGIES 2024; 5:e1. [PMID: 38510985 PMCID: PMC10952055 DOI: 10.1017/wtc.2023.26] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 11/07/2023] [Accepted: 12/02/2023] [Indexed: 03/22/2024]
Abstract
Telerehabilitation and robotics, either traditional rigid or soft, have been extensively studied and used to improve hand functionality after a stroke. However, a limited number of devices combined these two technologies to such a level of maturity that was possible to use them at the patients' home, unsupervised. Here we present a novel investigation that demonstrates the feasibility of a system that integrates a soft inflatable robotic glove, a cloud-connected software interface, and a telerehabilitation therapy. Ten chronic moderate-to-severe stroke survivors independently used the system at their home for 4 weeks, following a software-led therapy and being in touch with occupational therapists. Data from the therapy, including automatic assessments by the robot, were available to the occupational therapists in real-time, thanks to the cloud-connected capability of the system. The participants used the system intensively (about five times more movements per session than the standard care) for a total of more than 8 hr of therapy on average. We were able to observe improvements in standard clinical metrics (FMA +3.9 ± 4.0, p < .05, COPM-P + 2.5 ± 1.3, p < .05, COPM-S + 2.6 ± 1.9, p < .05, MAL-AOU +6.6 ± 6.5, p < .05) and range of motion (+88%) at the end of the intervention. Despite being small, these improvements sustained at follow-up, 2 weeks after the end of the therapy. These promising results pave the way toward further investigation for the deployment of combined soft robotic/telerehabilitive systems at-home for autonomous usage for stroke rehabilitation.
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Affiliation(s)
- Tommaso Proietti
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Kristin Nuckols
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Jesse Grupper
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Diogo Schwerz de Lucena
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Bianca Inirio
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | | | - Diana Wagner
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Tazzy Cole
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Christina Glover
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Sarah Mendelowitz
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Maxwell Herman
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Joan Breen
- Whittier Rehabilitation Hospital, Bradford, MA, USA
| | - David Lin
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- VA RR&D Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Department of VA Medical Center, Providence, RI, USA
| | - Conor Walsh
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
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Zhang M, Zhu F, Jia F, Wu Y, Wang B, Gao L, Chu F, Tang W. Efficacy of brain-computer interfaces on upper extremity motor function rehabilitation after stroke: A systematic review and meta-analysis. NeuroRehabilitation 2024; 54:199-212. [PMID: 38143387 DOI: 10.3233/nre-230215] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2023]
Abstract
BACKGROUND The recovery of upper limb function is crucial to the daily life activities of stroke patients. Brain-computer interface technology may have potential benefits in treating upper limb dysfunction. OBJECTIVE To systematically evaluate the efficacy of brain-computer interfaces (BCI) in the rehabilitation of upper limb motor function in stroke patients. METHODS Six databases up to July 2023 were reviewed according to the PRSIMA guidelines. Randomized controlled trials of BCI-based upper limb functional rehabilitation for stroke patients were selected for meta-analysis by pooling standardized mean difference (SMD) to summarize the evidence. The Cochrane risk of bias tool was used to assess the methodological quality of the included studies. RESULTS Twenty-five studies were included. The studies showed that BCI had a small effect on the improvement of upper limb function after the intervention. In terms of total duration of training, < 12 hours of training may result in better rehabilitation, but training duration greater than 12 hours suggests a non significant therapeutic effect of BCI training. CONCLUSION This meta-analysis suggests that BCI has a slight efficacy in improving upper limb function and has favorable long-term outcomes. In terms of total duration of training, < 12 hours of training may lead to better rehabilitation.
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Affiliation(s)
- Ming Zhang
- Department of Mechatronic Engineering, China University of Mining and Technology, Jiangsu, China
- The Affiliated Xuzhou Rehabilitation Hospital of Xuzhou Medical University, Xuzhou Medical University, Jiangsu, China
| | - Feilong Zhu
- College of Physical Education and Sports, Beijing Normal University, Beijing, China
| | - Fan Jia
- The Affiliated Xuzhou Rehabilitation Hospital of Xuzhou Medical University, Xuzhou Medical University, Jiangsu, China
| | - Yu Wu
- Department of Sports and Exercise Science, Zhejiang University, Hangzhou, China
| | - Bin Wang
- Departments of Rehabilitation Medicine, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Ling Gao
- The Affiliated Xuzhou Rehabilitation Hospital of Xuzhou Medical University, Xuzhou Medical University, Jiangsu, China
| | - Fengming Chu
- The Affiliated Xuzhou Rehabilitation Hospital of Xuzhou Medical University, Xuzhou Medical University, Jiangsu, China
| | - Wei Tang
- Department of Mechatronic Engineering, China University of Mining and Technology, Jiangsu, China
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Qu H, Zeng F, Tang Y, Shi B, Wang Z, Chen X, Wang J. The clinical effects of brain-computer interface with robot on upper-limb function for post-stroke rehabilitation: a meta-analysis and systematic review. Disabil Rehabil Assist Technol 2024; 19:30-41. [PMID: 35450498 DOI: 10.1080/17483107.2022.2060354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 03/26/2022] [Indexed: 10/18/2022]
Abstract
PURPOSE Many recent clinical studies have suggested that the combination of brain-computer interfaces (BCIs) can induce neurological recovery and improvement in motor function. In this review, we performed a systematic review and meta-analysis to evaluate the clinical effects of BCI-robot systems. METHODS The articles published from January 2010 to December 2020 have been searched by using the databases (EMBASE, PubMed, CINAHL, EBSCO, Web of Science and manual search). The single-group studies were qualitatively described, and only the controlled-trial studies were included for the meta-analysis. The mean difference (MD) of Fugl-Meyer Assessment (FMA) scores were pooled and the random-effects model method was used to perform the meta-analysis. The PRISMA criteria were followed in current review. RESULTS A total of 897 records were identified, eight single-group studies and 11 controlled-trial studies were included in our review. The systematic analysis indicated that the BCI-robot systems had a significant improvement on motor function recovery. The meta-analysis showed there were no statistic differences between BCI-robot groups and robot groups, neither in the immediate effects nor long-term effects (p > 0.05). CONCLUSION The use of BCI-robot systems has significant improvement on the motor function recovery of hemiparetic upper-limb, and there is a sustaining effect. The meta-analysis showed no statistical difference between the experimental group (BCI-robot) and the control group (robot). However, there are a few shortcomings in the experimental design of existing studies, more clinical trials need to be conducted, and the experimental design needs to be more rigorous.Implications for RehabilitationIn this review, we evaluated the clinical effects of brain-computer interface with robot on upper-limb function for post-stroke rehabilitation. After we screened the databases, 19 articles were included in this review. These articles all clinical trial research, they all used non-invasive brain-computer interfaces and upper-limb robot.We conducted the systematic review with nine articles, the result indicated that the BCI-robot system had a significant improvement on motor function recovery. Eleven articles were included for the meta-analysis, the result showed there were no statistic differences between BCI-robot groups and robot groups, neither in the immediate effects nor long-term effects.We thought the result of meta-analysis which showed no statistic difference was probably caused by the heterogenicity of clinical trial designs of these articles.We thought the BCI-robot systems are promising strategies for post-stroke rehabilitation. And we gave several suggestions for further research: (1) The experimental design should be more rigorous, and describe the experimental designs in detail, especially the control group intervention, to make the experiment replicability. (2) New evaluation criteria need to be established, more objective assessment such as biomechanical assessment, fMRI should be utilised as the primary outcome. (3) More clinical studies with larger sample size, novel external devices, and BCI systems need to be conducted to investigate the differences between BCI-robot system and other interventions. (4) Further research could shift the focus to the patients who are in subacute stage, to explore if the early BCI training can make a positive impact on cerebral cortical recovery.
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Affiliation(s)
- Hao Qu
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Feixiang Zeng
- Department of Rehabilitation Medicine, HuiZhou Third People's Hospital, Huizhou, China
| | - Yongbin Tang
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Bin Shi
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Zhijun Wang
- Department of Rehabilitation Medicine, FoShan Fifth People's Hospital, Guangdong, China
| | - Xiaokai Chen
- Department of Rehabilitation Medicine, HuiZhou Third People's Hospital, Huizhou, China
| | - Jing Wang
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
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15
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Quanyu W, Sheng D, Weige T, Lingjiao P, Xiaojie L. Research on MI EEG signal classification algorithm using multi-model fusion strategy coupling. Comput Methods Biomech Biomed Engin 2023:1-10. [PMID: 37982231 DOI: 10.1080/10255842.2023.2284091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 11/11/2023] [Indexed: 11/21/2023]
Abstract
To enhance the accuracy of motor imagery(MI)EEG signal recognition, two methods, namely power spectral density and wavelet packet decomposition combined with a common spatial pattern, were employed to explore the feature information in depth in MI EEG signals. The extracted MI EEG signal features were subjected to series feature fusion, and the F-test method was used to select features with higher information content. Here regarding the accuracy of MI EEG signal classification, we further proposed the Platt Scaling probability calibration method was used to calibrate the results obtained from six basic classifiers, namely random forest (RF), support vector machines (SVM), Logistic Regression (LR), Gaussian naïve bayes (GNB), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). From these 12 classifiers, three to four with higher accuracy were selected for model fusion. The proposed method was validated on Datasets 2a of the 4th International BCI Competition, achieving an average accuracy of MI EEG data of nine subjects reached 91.46%, which indicates that model fusion was an effective method to improve classification accuracy, and provides some reference value for the research on MI brain-machine interface.
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Affiliation(s)
- Wu Quanyu
- From School of Electrical & Information Engineering, Jiangsu University of Technology, Changzhou, Jiangsu, China
| | - Ding Sheng
- From School of Electrical & Information Engineering, Jiangsu University of Technology, Changzhou, Jiangsu, China
| | - Tao Weige
- From School of Electrical & Information Engineering, Jiangsu University of Technology, Changzhou, Jiangsu, China
| | - Pan Lingjiao
- From School of Electrical & Information Engineering, Jiangsu University of Technology, Changzhou, Jiangsu, China
| | - Liu Xiaojie
- From School of Electrical & Information Engineering, Jiangsu University of Technology, Changzhou, Jiangsu, China
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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: 3] [Impact Index Per Article: 3.0] [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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17
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Zhang Y, Qian K, Xie SQ, Shi C, Li J, Zhang ZQ. SSVEP-Based Brain-Computer Interface Controlled Robotic Platform With Velocity Modulation. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3448-3458. [PMID: 37624718 DOI: 10.1109/tnsre.2023.3308778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/27/2023]
Abstract
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been extensively studied due to many benefits, such as non-invasiveness, high information transfer rate, and ease of use. SSVEP-based BCI has been investigated in various applications by projecting brain signals to robot control commands. However, the movement direction and speed are generally fixed and prescribed, neglecting the user's requirement for velocity changes during practical implementations. In this study, we proposed a velocity modulation method based on stimulus brightness for controlling the robotic arm in the SSVEP-based BCI system. A stimulation interface was designed, incorporating flickers, target and a cursor workspace. The synchronization of the cursor and robotic arm does not require the subject's eye switch between the stimuli and the robot. The feature vector consists of the characteristics of the signal and the classification result. Subsequently, the Gaussian mixture model (GMM) and Bayesian inference were used to calculate the posterior probabilities that the signal came from a high or low brightness flicker. A brain-actuated speed function was designed by incorporating the posterior probability difference. Finally, the historical velocity was considered to determine the final velocity. To demonstrate the effectiveness of the proposed method, online experiments, including single- and multi-target reaching tasks, were conducted. The extensive experimental results validated the feasibility of the proposed method in reducing reaching time and achieving proximity to the target.
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18
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Bates M, Sunderam S. Hand-worn devices for assessment and rehabilitation of motor function and their potential use in BCI protocols: a review. Front Hum Neurosci 2023; 17:1121481. [PMID: 37484920 PMCID: PMC10357516 DOI: 10.3389/fnhum.2023.1121481] [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: 12/11/2022] [Accepted: 06/01/2023] [Indexed: 07/25/2023] Open
Abstract
Introduction Various neurological conditions can impair hand function. Affected individuals cannot fully participate in activities of daily living due to the lack of fine motor control. Neurorehabilitation emphasizes repetitive movement and subjective clinical assessments that require clinical experience to administer. Methods Here, we perform a review of literature focused on the use of hand-worn devices for rehabilitation and assessment of hand function. We paid particular attention to protocols that involve brain-computer interfaces (BCIs) since BCIs are gaining ground as a means for detecting volitional signals as the basis for interactive motor training protocols to augment recovery. All devices reviewed either monitor, assist, stimulate, or support hand and finger movement. Results A majority of studies reviewed here test or validate devices through clinical trials, especially for stroke. Even though sensor gloves are the most commonly employed type of device in this domain, they have certain limitations. Many such gloves use bend or inertial sensors to monitor the movement of individual digits, but few monitor both movement and applied pressure. The use of such devices in BCI protocols is also uncommon. Discussion We conclude that hand-worn devices that monitor both flexion and grip will benefit both clinical diagnostic assessment of function during treatment and closed-loop BCI protocols aimed at rehabilitation.
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19
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Autonomous grasping of 3-D objects by a vision-actuated robot arm using Brain–Computer Interface. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
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Ma ZZ, Wu JJ, Hua XY, Zheng MX, Xing XX, Ma J, Shan CL, Xu JG. Evidence of neuroplasticity with brain-computer interface in a randomized trial for post-stroke rehabilitation: a graph-theoretic study of subnetwork analysis. Front Neurol 2023; 14:1135466. [PMID: 37346164 PMCID: PMC10281191 DOI: 10.3389/fneur.2023.1135466] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 05/03/2023] [Indexed: 06/23/2023] Open
Abstract
Background Brain-computer interface (BCI) has been widely used for functional recovery after stroke. Understanding the brain mechanisms following BCI intervention to optimize BCI strategies is crucial for the benefit of stroke patients. Methods Forty-six patients with upper limb motor dysfunction after stroke were recruited and randomly divided into the control group or the BCI group. The primary outcome was measured by the assessment of Fugl-Meyer Assessment of Upper Extremity (FMA-UE). Meanwhile, we performed resting-state functional magnetic resonance imaging (rs-fMRI) in all patients, followed by independent component analysis (ICA) to identify functionally connected brain networks. Finally, we assessed the topological efficiency of both groups using graph-theoretic analysis in these brain subnetworks. Results The FMA-UE score of the BCI group was significantly higher than that of the control group after treatment (p = 0.035). From the network topology analysis, we first identified seven subnetworks from the rs-fMRI data. In the following analysis of subnetwork properties, small-world properties including γ (p = 0.035) and σ (p = 0.031) within the visual network (VN) decreased in the BCI group. For the analysis of the dorsal attention network (DAN), significant differences were found in assortativity (p = 0.045) between the groups. Additionally, the improvement in FMA-UE was positively correlated with the assortativity of the dorsal attention network (R = 0.498, p = 0.011). Conclusion Brain-computer interface can promote the recovery of upper limbs after stroke by regulating VN and DAN. The correlation trend of weak intensity proves that functional recovery in stroke patients is likely to be related to the brain's visuospatial processing ability, which can be used to optimize BCI strategies. Clinical Trial Registration The trial is registered in the Chinese Clinical Trial Registry, number ChiCTR2000034848. Registered 21 July 2020.
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Affiliation(s)
- Zhen-Zhen Ma
- Department of Rehabilitation Medicine, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent RehabilitationMinistry of Education, Shanghai, China
| | - Jia-Jia Wu
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent RehabilitationMinistry of Education, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xu-Yun Hua
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent RehabilitationMinistry of Education, Shanghai, China
- Department of Trauma and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Mou-Xiong Zheng
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent RehabilitationMinistry of Education, Shanghai, China
- Department of Trauma and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiang-Xin Xing
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent RehabilitationMinistry of Education, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jie Ma
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent RehabilitationMinistry of Education, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Chun-Lei Shan
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent RehabilitationMinistry of Education, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jian-Guang Xu
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent RehabilitationMinistry of Education, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Al-Qazzaz NK, Aldoori AA, Ali SHBM, Ahmad SA, Mohammed AK, Mohyee MI. EEG Signal Complexity Measurements to Enhance BCI-Based Stroke Patients' Rehabilitation. SENSORS (BASEL, SWITZERLAND) 2023; 23:3889. [PMID: 37112230 PMCID: PMC10141766 DOI: 10.3390/s23083889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 03/01/2023] [Accepted: 03/09/2023] [Indexed: 06/19/2023]
Abstract
The second leading cause of death and one of the most common causes of disability in the world is stroke. Researchers have found that brain-computer interface (BCI) techniques can result in better stroke patient rehabilitation. This study used the proposed motor imagery (MI) framework to analyze the electroencephalogram (EEG) dataset from eight subjects in order to enhance the MI-based BCI systems for stroke patients. The preprocessing portion of the framework comprises the use of conventional filters and the independent component analysis (ICA) denoising approach. Fractal dimension (FD) and Hurst exponent (Hur) were then calculated as complexity features, and Tsallis entropy (TsEn) and dispersion entropy (DispEn) were assessed as irregularity parameters. The MI-based BCI features were then statistically retrieved from each participant using two-way analysis of variance (ANOVA) to demonstrate the individuals' performances from four classes (left hand, right hand, foot, and tongue). The dimensionality reduction algorithm, Laplacian Eigenmap (LE), was used to enhance the MI-based BCI classification performance. Utilizing k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) classifiers, the groups of post-stroke patients were ultimately determined. The findings show that LE with RF and KNN obtained 74.48% and 73.20% accuracy, respectively; therefore, the integrated set of the proposed features along with ICA denoising technique can exactly describe the proposed MI framework, which may be used to explore the four classes of MI-based BCI rehabilitation. This study will help clinicians, doctors, and technicians make a good rehabilitation program for people who have had a stroke.
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Affiliation(s)
- Noor Kamal Al-Qazzaz
- Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad 47146, Iraq
| | - Alaa A. Aldoori
- Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad 47146, Iraq
| | - Sawal Hamid Bin Mohd Ali
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Selangor, Malaysia
- Centre of Advanced Electronic and Communication Engineering, Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Selangor, Malaysia
| | - Siti Anom Ahmad
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, UPM, Serdang 43400, Selangor, Malaysia
- Malaysian Research Institute of Ageing (MyAgeing), University Putra Malaysia, Serdang 43400, Selangor, Malaysia
| | - Ahmed Kazem Mohammed
- Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad 47146, Iraq
| | - Mustafa Ibrahim Mohyee
- Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad 47146, Iraq
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Jia T, Li C, Mo L, Qian C, Li W, Xu Q, Pan Y, Liu A, Ji L. Tailoring brain-machine interface rehabilitation training based on neural reorganization: towards personalized treatment for stroke patients. Cereb Cortex 2023; 33:3043-3052. [PMID: 35788284 PMCID: PMC10016036 DOI: 10.1093/cercor/bhac259] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 06/04/2022] [Accepted: 06/06/2022] [Indexed: 11/14/2022] Open
Abstract
Electroencephalogram (EEG)-based brain-machine interface (BMI) has the potential to enhance rehabilitation training efficiency, but it still remains elusive regarding how to design BMI training for heterogeneous stroke patients with varied neural reorganization. Here, we hypothesize that tailoring BMI training according to different patterns of neural reorganization can contribute to a personalized rehabilitation trajectory. Thirteen stroke patients were recruited in a 2-week personalized BMI training experiment. Clinical and behavioral measurements, as well as cortical and muscular activities, were assessed before and after training. Following treatment, significant improvements were found in motor function assessment. Three types of brain activation patterns were identified during BMI tasks, namely, bilateral widespread activation, ipsilesional focusing activation, and contralesional recruitment activation. Patients with either ipsilesional dominance or contralesional dominance can achieve recovery through personalized BMI training. Results indicate that personalized BMI training tends to connect the potentially reorganized brain areas with event-contingent proprioceptive feedback. It can also be inferred that personalization plays an important role in establishing the sensorimotor loop in BMI training. With further understanding of neural rehabilitation mechanisms, personalized treatment strategy is a promising way to improve the rehabilitation efficacy and promote the clinical use of rehabilitation robots and other neurotechnologies.
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Affiliation(s)
| | - Chong Li
- Corresponding authors: Division of Intelligent and Bio-mimetic Machinery, The State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China. ; Beijing Rehabilitation Hospital of Capital Medical University, Capital Medical University, Beijing 100144, China. ; Division of Intelligent and Bio-mimetic Machinery, The State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China.
| | - Linhong Mo
- Beijing Rehabilitation Hospital of Capital Medical University, Capital Medical University, Beijing 100144, China
| | - Chao Qian
- Division of Intelligent and Bio-mimetic Machinery, The State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
| | - Wei Li
- Division of Intelligent and Bio-mimetic Machinery, The State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
| | - Quan Xu
- Division of Intelligent and Bio-mimetic Machinery, The State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
- Department of Physical Medicine and Rehabilitation, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing 102218, China
| | - Yu Pan
- Department of Physical Medicine and Rehabilitation, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing 102218, China
| | - Aixian Liu
- Corresponding authors: Division of Intelligent and Bio-mimetic Machinery, The State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China. ; Beijing Rehabilitation Hospital of Capital Medical University, Capital Medical University, Beijing 100144, China. ; Division of Intelligent and Bio-mimetic Machinery, The State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China.
| | - Linhong Ji
- Corresponding authors: Division of Intelligent and Bio-mimetic Machinery, The State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China. ; Beijing Rehabilitation Hospital of Capital Medical University, Capital Medical University, Beijing 100144, China. ; Division of Intelligent and Bio-mimetic Machinery, The State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China.
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Berke Guney O, Ozkan H. Transfer learning of an ensemble of DNNs for SSVEP BCI spellers without user-specific training. J Neural Eng 2023; 20. [PMID: 36535036 DOI: 10.1088/1741-2552/acacca] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 12/19/2022] [Indexed: 12/23/2022]
Abstract
Objective.Steady-state visually evoked potentials (SSVEPs), measured with electroencephalogram (EEG), yield decent information transfer rates (ITRs) in brain-computer interface (BCI) spellers. However, the current high performing SSVEP BCI spellers in the literature require an initial lengthy and tiring user-specific training for each new user for system adaptation, including data collection with EEG experiments, algorithm training and calibration (all are before the actual use of the system). This impedes the widespread use of BCIs. To ensure practicality, we propose a novel target identification method based on an ensemble of deep neural networks (DNNs), which does not require any sort of user-specific training.Approach.We exploit already-existing literature datasets from participants of previously conducted EEG experiments to train a global target identifier DNN first, which is then fine-tuned to each participant. We transfer this ensemble of fine-tuned DNNs to the new user instance, determine thekmost representative DNNs according to the participants' statistical similarities to the new user, and predict the target character through a weighted combination of the ensemble predictions.Main results.The proposed method significantly outperforms all the state-of-the-art alternatives for all stimulation durations in [0.2-1.0] s on two large-scale benchmark and BETA datasets, and achieves impressive 155.51 bits/min and 114.64 bits/min ITRs. Code is available for reproducibility:https://github.com/osmanberke/Ensemble-of-DNNs.Significance.Our Ensemble-DNN method has the potential to promote the practical widespread deployment of BCI spellers in daily lives as we provide the highest performance while enabling the immediate system use without any user-specific training.
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Affiliation(s)
- Osman Berke Guney
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, United States of America
| | - Huseyin Ozkan
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
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Hernández Echarren A, Sánchez Cabeza Á. [Hand robotic devices in neurorehabilitation: A systematic review on the feasibility and effectiveness of stroke rehabilitation]. Rehabilitacion (Madr) 2023; 57:100758. [PMID: 36319483 DOI: 10.1016/j.rh.2022.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 07/18/2022] [Accepted: 08/01/2022] [Indexed: 11/22/2022]
Abstract
Robot-assisted therapy is a relatively new intervention, increasingly used in the rehabilitation treatment of stroke patients. It allows to increase the number of repetitions in the performance of specific tasks movements. For this review, a search was carried out between August and October 2021 in the PubMed, Web of Science, Scopus, Cochrane, PEDro and OTseeker databases, selecting a total of six randomized controlled trials where robot-assisted hand therapy was used in stroke rehabilitation. Studies agree that robot-assisted hand therapy has benefits in all phases of stroke rehabilitation that translate into motor and functional improvements of the upper limb and improvements in hemispatial neglect.
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Affiliation(s)
- A Hernández Echarren
- Departamento de Fisioterapia, Terapia Ocupacional, Rehabilitación y Medicina Física, Facultad de Ciencias de la Salud, Universidad Rey Juan Carlos, Madrid, España.
| | - Á Sánchez Cabeza
- Departamento de Fisioterapia, Terapia Ocupacional, Rehabilitación y Medicina Física, Facultad de Ciencias de la Salud, Universidad Rey Juan Carlos, Madrid, España
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25
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Fu J, Chen S, Jia J. Sensorimotor Rhythm-Based Brain-Computer Interfaces for Motor Tasks Used in Hand Upper Extremity Rehabilitation after Stroke: A Systematic Review. Brain Sci 2022; 13:brainsci13010056. [PMID: 36672038 PMCID: PMC9856697 DOI: 10.3390/brainsci13010056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/05/2022] [Accepted: 12/25/2022] [Indexed: 12/29/2022] Open
Abstract
Brain-computer interfaces (BCIs) are becoming more popular in the neurological rehabilitation field, and sensorimotor rhythm (SMR) is a type of brain oscillation rhythm that can be captured and analyzed in BCIs. Previous reviews have testified to the efficacy of the BCIs, but seldom have they discussed the motor task adopted in BCIs experiments in detail, as well as whether the feedback is suitable for them. We focused on the motor tasks adopted in SMR-based BCIs, as well as the corresponding feedback, and searched articles in PubMed, Embase, Cochrane library, Web of Science, and Scopus and found 442 articles. After a series of screenings, 15 randomized controlled studies were eligible for analysis. We found motor imagery (MI) or motor attempt (MA) are common experimental paradigms in EEG-based BCIs trials. Imagining/attempting to grasp and extend the fingers is the most common, and there were multi-joint movements, including wrist, elbow, and shoulder. There were various types of feedback in MI or MA tasks for hand grasping and extension. Proprioception was used more frequently in a variety of forms. Orthosis, robot, exoskeleton, and functional electrical stimulation can assist the paretic limb movement, and visual feedback can be used as primary feedback or combined forms. However, during the recovery process, there are many bottleneck problems for hand recovery, such as flaccid paralysis or opening the fingers. In practice, we should mainly focus on patients' difficulties, and design one or more motor tasks for patients, with the assistance of the robot, FES, or other combined feedback, to help them to complete a grasp, finger extension, thumb opposition, or other motion. Future research should focus on neurophysiological changes and functional improvements and further elaboration on the changes in neurophysiology during the recovery of motor function.
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Affiliation(s)
- Jianghong Fu
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Shugeng Chen
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jie Jia
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Shanghai 200040, China
- Correspondence: ; Tel./Fax: +86-021-5288-7820
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26
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Brain-machine Interface (BMI)-based Neurorehabilitation for Post-stroke Upper Limb Paralysis. Keio J Med 2022; 71:82-92. [PMID: 35718470 DOI: 10.2302/kjm.2022-0002-oa] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Because recovery from upper limb paralysis after stroke is challenging, compensatory approaches have been the main focus of upper limb rehabilitation. However, based on fundamental and clinical research indicating that the brain has a far greater potential for plastic change than previously thought, functional restorative approaches have become increasingly common. Among such interventions, constraint-induced movement therapy, task-specific training, robotic therapy, neuromuscular electrical stimulation (NMES), mental practice, mirror therapy, and bilateral arm training are recommended in recently published stroke guidelines. For severe upper limb paralysis, however, no effective therapy has yet been established. Against this background, there is growing interest in applying brain-machine interface (BMI) technologies to upper limb rehabilitation. Increasing numbers of randomized controlled trials have demonstrated the effectiveness of BMI neurorehabilitation, and several meta-analyses have shown medium to large effect sizes with BMI therapy. Subgroup analyses indicate higher intervention effects in the subacute group than the chronic group, when using movement attempts as the BMI-training trigger task rather than using motor imagery, and using NMES as the external device compared with using other devices. The Keio BMI team has developed an electroencephalography-based neurorehabilitation system and has published clinical and basic studies demonstrating its effectiveness and neurophysiological mechanisms. For its wider clinical application, the positioning of BMI therapy in upper limb rehabilitation needs to be clarified, BMI needs to be commercialized as an easy-to-use and cost-effective medical device, and training systems for rehabilitation professionals need to be developed. A technological breakthrough enabling selective modulation of neural circuits is also needed.
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27
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A Review of Brain Activity and EEG-Based Brain-Computer Interfaces for Rehabilitation Application. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 9:bioengineering9120768. [PMID: 36550974 PMCID: PMC9774292 DOI: 10.3390/bioengineering9120768] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 11/29/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022]
Abstract
Patients with severe CNS injuries struggle primarily with their sensorimotor function and communication with the outside world. There is an urgent need for advanced neural rehabilitation and intelligent interaction technology to provide help for patients with nerve injuries. Recent studies have established the brain-computer interface (BCI) in order to provide patients with appropriate interaction methods or more intelligent rehabilitation training. This paper reviews the most recent research on brain-computer-interface-based non-invasive rehabilitation systems. Various endogenous and exogenous methods, advantages, limitations, and challenges are discussed and proposed. In addition, the paper discusses the communication between the various brain-computer interface modes used between severely paralyzed and locked patients and the surrounding environment, particularly the brain-computer interaction system utilizing exogenous (induced) EEG signals (such as P300 and SSVEP). This discussion reveals with an examination of the interface for collecting EEG signals, EEG components, and signal postprocessing. Furthermore, the paper describes the development of natural interaction strategies, with a focus on signal acquisition, data processing, pattern recognition algorithms, and control techniques.
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28
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Ou W, Xiao S, Zhu C, Han W, Zhang Q. An overview of brain-like computing: Architecture, applications, and future trends. Front Neurorobot 2022; 16:1041108. [PMID: 36506817 PMCID: PMC9730831 DOI: 10.3389/fnbot.2022.1041108] [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: 09/10/2022] [Accepted: 10/31/2022] [Indexed: 11/25/2022] Open
Abstract
With the development of technology, Moore's law will come to an end, and scientists are trying to find a new way out in brain-like computing. But we still know very little about how the brain works. At the present stage of research, brain-like models are all structured to mimic the brain in order to achieve some of the brain's functions, and then continue to improve the theories and models. This article summarizes the important progress and status of brain-like computing, summarizes the generally accepted and feasible brain-like computing models, introduces, analyzes, and compares the more mature brain-like computing chips, outlines the attempts and challenges of brain-like computing applications at this stage, and looks forward to the future development of brain-like computing. It is hoped that the summarized results will help relevant researchers and practitioners to quickly grasp the research progress in the field of brain-like computing and acquire the application methods and related knowledge in this field.
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Affiliation(s)
- Wei Ou
- The School of Cyberspace Security, Hainan University, Hainan, China
- Henan Key Laboratory of Network Cryptography Technology, Zhengzhou, China
| | - Shitao Xiao
- The School of Computer Science and Technology, Hainan, China
| | - Chengyu Zhu
- The School of Cyberspace Security, Hainan University, Hainan, China
| | - Wenbao Han
- The School of Cyberspace Security, Hainan University, Hainan, China
| | - Qionglu Zhang
- State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China
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29
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Cao L, Wang W, Huang C, Xu Z, Wang H, Jia J, Chen S, Dong Y, Fan C, de Albuquerque VHC. An Effective Fusing Approach by Combining Connectivity Network Pattern and Temporal-Spatial Analysis for EEG-Based BCI Rehabilitation. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2264-2274. [PMID: 35969547 DOI: 10.1109/tnsre.2022.3198434] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Motor-modality-based brain computer interface (BCI) could promote the neural rehabilitation for stroke patients. Temporal-spatial analysis was commonly used for pattern recognition in this task. This paper introduced a novel connectivity network analysis for EEG-based feature selection. The network features of connectivity pattern not only captured the spatial activities responding to motor task, but also mined the interactive pattern among these cerebral regions. Furthermore, the effective combination between temporal-spatial analysis and network analysis was evaluated for improving the performance of BCI classification (81.7%). And the results demonstrated that it could raise the classification accuracies for most of patients (6 of 7 patients). This proposed method was meaningful for developing the effective BCI training program for stroke rehabilitation.
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30
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Xie YL, Yang YX, Jiang H, Duan XY, Gu LJ, Qing W, Zhang B, Wang YX. Brain-machine interface-based training for improving upper extremity function after stroke: A meta-analysis of randomized controlled trials. Front Neurosci 2022; 16:949575. [PMID: 35992923 PMCID: PMC9381818 DOI: 10.3389/fnins.2022.949575] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022] Open
Abstract
Background Upper extremity dysfunction after stroke is an urgent clinical problem that greatly affects patients' daily life and reduces their quality of life. As an emerging rehabilitation method, brain-machine interface (BMI)-based training can extract brain signals and provide feedback to form a closed-loop rehabilitation, which is currently being studied for functional restoration after stroke. However, there is no reliable medical evidence to support the effect of BMI-based training on upper extremity function after stroke. This review aimed to evaluate the efficacy and safety of BMI-based training for improving upper extremity function after stroke, as well as potential differences in efficacy of different external devices. Methods English-language literature published before April 1, 2022, was searched in five electronic databases using search terms including “brain-computer/machine interface”, “stroke” and “upper extremity.” The identified articles were screened, data were extracted, and the methodological quality of the included trials was assessed. Meta-analysis was performed using RevMan 5.4.1 software. The GRADE method was used to assess the quality of the evidence. Results A total of 17 studies with 410 post-stroke patients were included. Meta-analysis showed that BMI-based training significantly improved upper extremity motor function [standardized mean difference (SMD) = 0.62; 95% confidence interval (CI) (0.34, 0.90); I2 = 38%; p < 0.0001; n = 385; random-effects model; moderate-quality evidence]. Subgroup meta-analysis indicated that BMI-based training significantly improves upper extremity motor function in both chronic [SMD = 0.68; 95% CI (0.32, 1.03), I2 = 46%; p = 0.0002, random-effects model] and subacute [SMD = 1.11; 95%CI (0.22, 1.99); I2 = 76%; p = 0.01; random-effects model] stroke patients compared with control interventions, and using functional electrical stimulation (FES) [SMD = 1.11; 95% CI (0.67, 1.54); I2 = 11%; p < 0.00001; random-effects model]or visual feedback [SMD = 0.66; 95% CI (0.2, 1.12); I2 = 4%; p = 0.005; random-effects model;] as the feedback devices in BMI training was more effective than using robot. In addition, BMI-based training was more effective in improving patients' activities of daily living (ADL) than control interventions [SMD = 1.12; 95% CI (0.65, 1.60); I2 = 0%; p < 0.00001; n = 80; random-effects model]. There was no statistical difference in the dropout rate and adverse effects between the BMI-based training group and the control group. Conclusion BMI-based training improved upper limb motor function and ADL in post-stroke patients. BMI combined with FES or visual feedback may be a better combination for functional recovery than robot. BMI-based trainings are well-tolerated and associated with mild adverse effects.
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Affiliation(s)
- Yu-lei Xie
- Department of Rehabilitation Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
- Department of Rehabilitation Medicine, Capital Medical University, Beijing, China
| | - Yu-xuan Yang
- Department of Rehabilitation Medicine, The Second Clinical Hospital of North Sichuan Medical College, Nanchong Central Hospital, Nanchong, China
| | - Hong Jiang
- Department of Rehabilitation Medicine, Xichong County People's Hospital, Nanchong Central Hospital, Nanchong, China
| | - Xing-Yu Duan
- Department of Rehabilitation Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Li-jing Gu
- Department of Rehabilitation Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Wu Qing
- Department of Rehabilitation Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Bo Zhang
- Department of Rehabilitation Medicine, The Second Clinical Hospital of North Sichuan Medical College, Nanchong Central Hospital, Nanchong, China
- Bo Zhang
| | - Yin-xu Wang
- Department of Rehabilitation Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
- *Correspondence: Yin-xu Wang
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31
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Mane R, Wu Z, Wang D. Poststroke motor, cognitive and speech rehabilitation with brain-computer interface: a perspective review. Stroke Vasc Neurol 2022; 7:svn-2022-001506. [PMID: 35853669 PMCID: PMC9811566 DOI: 10.1136/svn-2022-001506] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 06/17/2022] [Indexed: 01/17/2023] Open
Abstract
Brain-computer interface (BCI) technology translates brain activity into meaningful commands to establish a direct connection between the brain and the external world. Neuroscientific research in the past two decades has indicated a tremendous potential of BCI systems for the rehabilitation of patients suffering from poststroke impairments. By promoting the neuronal recovery of the damaged brain networks, BCI systems have achieved promising results for the recovery of poststroke motor, cognitive, and language impairments. Also, several assistive BCI systems that provide alternative means of communication and control to severely paralysed patients have been proposed to enhance patients' quality of life. In this article, we present a perspective review of the recent advances and challenges in the BCI systems used in the poststroke rehabilitation of motor, cognitive, and communication impairments.
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Affiliation(s)
| | | | - David Wang
- Neurovascular Division, Department of Neurology, Barrow Neurological Institute, Phoenix, Arizona, USA
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32
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Guo N, Wang X, Duanmu D, Huang X, Li X, Fan Y, Li H, Liu Y, Yeung EHK, To MKT, Gu J, Wan F, Hu Y. SSVEP-Based Brain Computer Interface Controlled Soft Robotic Glove for Post-Stroke Hand Function Rehabilitation. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1737-1744. [PMID: 35731756 DOI: 10.1109/tnsre.2022.3185262] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Soft robotic glove with brain computer interfaces (BCI) control has been used for post-stroke hand function rehabilitation. Motor imagery (MI) based BCI with robotic aided devices has been demonstrated as an effective neural rehabilitation tool to improve post-stroke hand function. It is necessary for a user of MI-BCI to receive a long time training, while the user usually suffers unsuccessful and unsatisfying results in the beginning. To propose another non-invasive BCI paradigm rather than MI-BCI, steady-state visually evoked potentials (SSVEP) based BCI was proposed as user intension detection to trigger the soft robotic glove for post-stroke hand function rehabilitation. Thirty post-stroke patients with impaired hand function were randomly and equally divided into three groups to receive conventional, robotic, and BCI-robotic therapy in this randomized control trial (RCT). Clinical assessment of Fugl-Meyer Motor Assessment of Upper Limb (FMA-UL), Wolf Motor Function Test (WMFT) and Modified Ashworth Scale (MAS) were performed at pre-training, post-training and three months follow-up. In comparing to other groups, The BCI-robotic group showed significant improvement after training in FMA full score (10.05±8.03, p=0.001), FMA shoulder/elbow (6.2±5.94, p=0.0004) and FMA wrist/hand (4.3±2.83, p=0.007), and WMFT (5.1±5.53, p=0.037). The improvement of FMA was significantly correlated with BCI accuracy (r=0.714, p=0.032). Recovery of hand function after rehabilitation of SSVEP-BCI controlled soft robotic glove showed better result than solely robotic glove rehabilitation, equivalent efficacy as results from previous reported MI-BCI robotic hand rehabilitation. It proved the feasibility of SSVEP-BCI controlled soft robotic glove in post-stroke hand function rehabilitation.
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Wang J, Wang W, Ren S, Shi W, Hou ZG. Neural Correlates of Single-Task Versus Cognitive-Motor Dual-Task Training. IEEE Trans Cogn Dev Syst 2022. [DOI: 10.1109/tcds.2021.3053050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Jiaxing Wang
- State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Weiqun Wang
- State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Shixin Ren
- State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Weiguo Shi
- State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zeng-Guang Hou
- State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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Ma W, Gong Y, Xue H, Liu Y, Lin X, Zhou G, Li Y. A lightweight and accurate double-branch neural network for four-class motor imagery classification. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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35
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Sierotowicz M, Lotti N, Nell L, Missiroli F, Alicea R, Zhang X, Xiloyannis M, Rupp R, Papp E, Krzywinski J, Castellini C, Masia L. EMG-Driven Machine Learning Control of a Soft Glove for Grasping Assistance and Rehabilitation. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2021.3140055] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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36
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Lee M, Kim YH, Lee SW. Motor Impairment in Stroke Patients is Associated with Network Properties During Consecutive Motor Imagery. IEEE Trans Biomed Eng 2022; 69:2604-2615. [PMID: 35171761 DOI: 10.1109/tbme.2022.3151742] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Our study aimed to predict the Fugl-Meyer assessment (FMA) upper limb using network properties during motor imagery using electroencephalography (EEG) signals. METHODS The subjects performed a finger tapping imagery task according to consecutive cues. We measured the weighted phase lag index (wPLI) as functional connectivity and directed transfer function (DTF) as causal connectivity in healthy controls and stroke patients. The network properties based on the wPLI and DTF were calculated. We predicted the FMA upper limb using partial least squares regression. RESULTS A higher DTF in the mu band was observed in stroke patients than in healthy controls. Notably, the difference in local properties at node F3 was negatively correlated with motor impairment in stroke patients. Finally, using significant network properties based on the wPLI and DTF, we predicted motor impairments using the FMA upper limb with a root-mean-square error of 1.68 (R2 = 0.97). This outperformed the state-of-the-art predictors. CONCLUSION These findings demonstrate that network properties based on functional and causal connectivity were highly associated with motor function in stroke patients. SIGNIFICANCE Our network properties can help calculate the predictor of motor impairments in stroke rehabilitation and provide insight into the neural correlates related to motor function based on EEG after reorganization induced by stroke.
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Yang W, Zhang X, Li Z, Zhang Q, Xue C, Huai Y. The Effect of Brain–Computer Interface Training on Rehabilitation of Upper Limb Dysfunction After Stroke: A Meta-Analysis of Randomized Controlled Trials. Front Neurosci 2022; 15:766879. [PMID: 35197817 PMCID: PMC8859107 DOI: 10.3389/fnins.2021.766879] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 12/13/2021] [Indexed: 11/13/2022] Open
Abstract
Background Upper limb motor dysfunction caused by stroke greatly affects the daily life of patients, significantly reduces their quality of life, and places serious burdens on society. As an emerging rehabilitation training method, brain–computer interface (BCI)–based training can provide closed-loop rehabilitation and is currently being applied to the restoration of upper limb function following stroke. However, because of the differences in the type of experimental clinical research, the quality of the literature varies greatly, and debate around the efficacy of BCI for the rehabilitation of upper limb dysfunction after stroke has continued. Objective We aimed to provide medical evidence-based support for BCI in the treatment of upper limb dysfunction after stroke by conducting a meta-analysis of relevant clinical studies. Methods The search terms used to retrieve related articles included “brain-computer interface,” “stroke,” and “upper extremity.” A total of 13 randomized controlled trials involving 258 participants were retrieved from five databases (PubMed, Cochrane Library, Science Direct, MEDLINE, and Web of Science), and RevMan 5.3 was used for data analysis. Results The total effect size for BCI training on upper limb motor function of post-stroke patients was 0.56 (95% CI: 0.29–0.83). Subgroup analysis indicated that the standard mean differences of BCI training on upper limb motor function of subacute stroke patients and chronic stroke patients were 1.10 (95% CI: 0.20–2.01) and 0.51 (95% CI: 0.09–0.92), respectively (p = 0.24). Conclusion Brain–computer interface training was shown to be effective in promoting upper limb motor function recovery in post-stroke patients, and the effect size was moderate.
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38
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Su E, Cai S, Xie L, Li H, Schultz T. STAnet: A Spatiotemporal Attention Network for Decoding Auditory Spatial Attention from EEG. IEEE Trans Biomed Eng 2022; 69:2233-2242. [PMID: 34982671 DOI: 10.1109/tbme.2022.3140246] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Humans are able to localize the source of a sound. This enables them to direct attention to a particular speaker in a cocktail party. Psycho-acoustic studies show that the sensory cortices of the human brain respond to the location of sound sources differently, and the auditory attention itself is a dynamic and temporally based brain activity. In this work, we seek to build a computational model which uses both spatial and temporal information manifested in EEG signals for auditory spatial attention detection (ASAD). METHODS We propose an end-to-end spatiotemporal attention network, denoted as STAnet, to detect auditory spatial attention from EEG. The STAnet is designed to assign differentiated weights dynamically to EEG channels through a spatial attention mechanism, and to temporal patterns in EEG signals through a temporal attention mechanism. RESULTS We report the ASAD experiments on two publicly available datasets. The STAnet outperforms other competitive models by a large margin under various experimental conditions. Its attention decision for 1-second decision window outperforms that of the state-of-the-art techniques for 10-second decision window. Experimental results also demonstrate that the STAnet achieves competitive performance on EEG signals ranging from 64 to as few as 16 channels. CONCLUSION This study provides evidence suggesting that efficient low-density EEG online decoding is within reach. SIGNIFICANCE This study also marks an important step towards the practical implementation of ASAD in real life applications.
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Chen W, Li G, Li N, Wang W, Yu P, Wang R, Xue X, Zhao X, Liu L. Soft Exoskeleton With Fully Actuated Thumb Movements for Grasping Assistance. IEEE T ROBOT 2022. [DOI: 10.1109/tro.2022.3148909] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Jia X, Song Y, Yang L, Xie L. Joint spatial and temporal features extraction for multi-classification of motor imagery EEG. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103247] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Mansour S, Ang KK, Nair KP, Phua KS, Arvaneh M. Efficacy of Brain-Computer Interface and the Impact of Its Design Characteristics on Poststroke Upper-limb Rehabilitation: A Systematic Review and Meta-analysis of Randomized Controlled Trials. Clin EEG Neurosci 2022; 53:79-90. [PMID: 33913351 PMCID: PMC8619716 DOI: 10.1177/15500594211009065] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 02/03/2021] [Accepted: 03/12/2021] [Indexed: 11/15/2022]
Abstract
Background. A number of recent randomized controlled trials reported the efficacy of brain-computer interface (BCI) for upper-limb stroke rehabilitation compared with other therapies. Despite the encouraging results reported, there is a significant variance in the reported outcomes. This paper aims to investigate the effectiveness of different BCI designs on poststroke upper-limb rehabilitation. Methods. The effect sizes of pooled and individual studies were assessed by computing Hedge's g values with a 95% confidence interval. Subgroup analyses were also performed to examine the impact of different BCI designs on the treatment effect. Results. The study included 12 clinical trials involving 298 patients. The analysis showed that the BCI yielded significant superior short-term and long-term efficacy in improving the upper-limb motor function compared to the control therapies (Hedge's g = 0.73 and 0.33, respectively). Based on our subgroup analyses, the BCI studies that used the intention of movement had a higher effect size compared to those used motor imagery (Hedge's g = 1.21 and 0.55, respectively). The BCI studies using band power features had a significantly higher effect size than those using filter bank common spatial patterns features (Hedge's g = 1.25 and - 0.23, respectively). Finally, the studies that used functional electrical stimulation as the BCI feedback had the highest effect size compared to other devices (Hedge's g = 1.2). Conclusion. This meta-analysis confirmed the effectiveness of BCI for upper-limb rehabilitation. Our findings support the use of band power features, the intention of movement, and the functional electrical stimulation in future BCI designs for poststroke upper-limb rehabilitation.
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Affiliation(s)
- Salem Mansour
- Department of Automatic Control and Systems Engineering, University
of Sheffield, UK
| | - Kai Keng Ang
- Agency for Science Technology and
Research, Institute for Infocomm Research, Singapore, Singapore
- School of Computer Science and Engineering, Nanyang Technological
University, Singapore
| | - Krishnan P.S. Nair
- School of Computer Science and Engineering, Nanyang Technological
University, Singapore
| | - Kok Soon Phua
- Agency for Science Technology and
Research, Institute for Infocomm Research, Singapore, Singapore
| | - Mahnaz Arvaneh
- Department of Automatic Control and Systems Engineering, University
of Sheffield, UK
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Nojima I, Sugata H, Takeuchi H, Mima T. Brain-Computer Interface Training Based on Brain Activity Can Induce Motor Recovery in Patients With Stroke: A Meta-Analysis. Neurorehabil Neural Repair 2021; 36:83-96. [PMID: 34958261 DOI: 10.1177/15459683211062895] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Brain-computer interface (BCI) is a procedure involving brain activity in which neural status is provided to the participants for self-regulation. The current review aims to evaluate the effect sizes of clinical studies investigating the use of BCI-based rehabilitation interventions in restoring upper extremity function and effective methods to detect brain activity for motor recovery. METHODS A computerized search of MEDLINE, CENTRAL, Web of Science, and PEDro was performed to identify relevant articles. We selected clinical trials that used BCI-based training for post-stroke patients and provided motor assessment scores before and after the intervention. The pooled standardized mean differences of BCI-based training were calculated using the random-effects model. RESULTS We initially identified 655 potentially relevant articles; finally, 16 articles fulfilled the inclusion criteria, involving 382 participants. A significant effect of neurofeedback intervention for the paretic upper limb was observed (standardized mean difference = .48, [.16-.80], P = .006). However, the effect estimates were moderately heterogeneous among the studies (I2 = 45%, P = .03). Subgroup analysis of the method of measurement of brain activity indicated the effectiveness of the algorithm focusing on sensorimotor rhythm. CONCLUSION This meta-analysis suggested that BCI-based training was superior to conventional interventions for motor recovery of the upper limbs in patients with stroke. However, the results are not conclusive because of a high risk of bias and a large degree of heterogeneity due to the differences in the BCI interventions and the participants; therefore, further studies involving larger cohorts are required to confirm these results.
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Affiliation(s)
- Ippei Nojima
- Department of Physical Therapy, 84161Shinshu University School of Health Sciences, Matsumoto, Japan
| | - Hisato Sugata
- Faculty of Welfare and Health Science, 6339Oita University, Oita, Japan
| | - Hiroki Takeuchi
- National Hospital Organization, 73721Higashinagoya National Hospital, Nagoya, Japan
| | - Tatsuya Mima
- Graduate School of Core Ethics and Frontier Sciences, 316844Ritsumeikan University, Kyoto, Japan
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Niu X, Lu N, Kang J, Cui Z. Knowledge-driven feature component interpretable network for motor imagery classification. J Neural Eng 2021; 19. [PMID: 34942608 DOI: 10.1088/1741-2552/ac463a] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 12/23/2021] [Indexed: 11/11/2022]
Abstract
OBJECTIVE End-to-end convolution neural network (CNN) has achieved great success in motor imagery classification without manual feature design. However, all the existing deep network solutions are purely data-driven and lack interpretability, which makes it impossible to discover insightful knowledge from the learnt features, not to mention to design specific network structure. The heavy computational cost of CNN also makes it challenging for real time application along with high classification performance. APPROACH To address these problems, a novel Knowledge-driven Feature Component Interpretable Network (KFCNet) was proposed, which combines spatial and temporal convolution in analogy to ICA and power spectrum pipeline. Prior frequency band knowledge of sensory motor rhythms (SMR) has been formulated as band-pass linear-phase digit FIR filters to initialize the temporal convolution kernels to enable knowledge driven mechanism. To avoid signal distortion and achieve linear phase and unimodality of filters, a symmetry loss is proposed, which is used in combination with the cross-entropy classification loss for training. Besides the general prior knowledge, subject specific time-frequency property of ERDS (event-related desynchronization and synchronization) has been employed to construct and initialize the network with significantly fewer parameters. MAIN RESULTS Comparison experiments on two public datasets have been performed. Interpretable feature components could be observed in the trained model. The physically meaningful observation could efficiently assist the network structure design. Excellent classification performance on motor imagery has been obtained. SIGNIFICANCE The performance of KFCNet is comparative to the state-of-the-art methods but with much fewer parameters and makes real time application possible.
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Affiliation(s)
- Xu Niu
- Xi'an Jiaotong University, 28# Xianning West Street, Xi'an, Shaanxi, China, Xi'an, Shaanxi, 710049, CHINA
| | - Na Lu
- Systems Engineering Institute, Xi'an Jiaotong University, 28# Xianning West Street, Xi'an, Shaanxi, China, Xi'an, Shaanxi, 710049, CHINA
| | - Jianghong Kang
- Xi'an Jiaotong University, 28# Xianning West Street, Xi'an, Shaanxi, China, Xi'an, Shaanxi, 710049, CHINA
| | - Zhiyan Cui
- Xi'an Jiaotong University, Systems Engineering Institute, Xi'an Jiaotong University, 28#Xianning West Street, Xi'an, Shaanxi, 710049, CHINA
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Nasrallah FA, Mohamed AZ, Yap HK, Lai HS, Yeow CH, Lim JH. Effect of proprioceptive stimulation using a soft robotic glove on motor activation and brain connectivity in stroke survivors. J Neural Eng 2021; 18:066049. [PMID: 34933283 DOI: 10.1088/1741-2552/ac456c] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Soft-robotic-assisted training may improve motor function during post-stroke recovery, but the underlying physiological changes are not clearly understood. We applied a single-session of intensive proprioceptive stimulation to stroke survivors using a soft robotic glove to delineate its short-term influence on brain functional activity and connectivity. APPROACH In this study, we utilized task-based and resting-state functional magnetic resonance imaging (fMRI) to characterize the changes in different brain networks following a soft robotic intervention. Nine stroke patients with hemiplegic upper limb engaged in resting-state and motor-task fMRI. The motor tasks comprised two conditions: active movement of fingers (active task) and glove-assisted active movement using a robotic glove (glove-assisted task), both with visual instruction. Each task was performed using bilateral hands simultaneously or the affected hand only. The same set of experiments was repeated following a 30-minute treatment of continuous passive motion (CPM) using a robotic glove. MAIN RESULTS On simultaneous bimanual movement, increased activation of supplementary motor area (SMA) and primary motor area (M1) were observed after CPM treatment compared to the pre-treatment condition, both in active and glove-assisted task. However, when performing the tasks solely using the affected hand, the phenomena of increased activity were not observed either in active or glove-assisted task. The comparison of the resting-state fMRI between before and after CPM showed the connectivity of the supramarginal gyrus and SMA was increased in the somatosensory network and salience network. SIGNIFICANCE This study demonstrates how passive motion exercise activates M1 and SMA in the post-stroke brain. The effective proprioceptive motor integration seen in bimanual exercise in contrast to the unilateral affected hand exercise suggests that the unaffected hemisphere might reconfigure connectivity to supplement damaged neural networks in the affected hemisphere. The somatosensory modulation rendered by the intense proprioceptive stimulation would affect the motor learning process in stroke survivors.
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Affiliation(s)
- Fatima A Nasrallah
- The University of Queensland Queensland Brain Institute, The University of Queensland, Brisbane, Saint Lucia, Queensland, 4072, AUSTRALIA
| | - Abdalla Z Mohamed
- The University of Queensland Queensland Brain Institute, The University of Queensland, Brisbane, Australia., Saint Lucia, Queensland, 4072, AUSTRALIA
| | - Hong Kai Yap
- Roceso Technologies, 83 Science Park Dr #04-01, Singapore, 118258, SINGAPORE
| | - Hwa Sen Lai
- National University of Singapore, Biomedical Engineering, Singapore, 119260, SINGAPORE
| | - Chen-Hua Yeow
- National University of Singapore, Biomedical Engineering, Singapore, 119260, SINGAPORE
| | - Jeong Hoon Lim
- School of Medicine, Medicine, National University of Singapore, NUHS Tower block level 10 1E, Kent Ridge Road, Singapore, Singapore, 119228, SINGAPORE
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Khan MA, Saibene M, Das R, Brunner IC, Puthusserypady S. Emergence of flexible technology in developing advanced systems for post-stroke rehabilitation: a comprehensive review. J Neural Eng 2021; 18. [PMID: 34736239 DOI: 10.1088/1741-2552/ac36aa] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 11/04/2021] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Stroke is one of the most common neural disorders, which causes physical disabilities and motor impairments among its survivors. Several technologies have been developed for providing stroke rehabilitation and to assist the survivors in performing their daily life activities. Currently, the use of flexible technology (FT) for stroke rehabilitation systems is on a rise that allows the development of more compact and lightweight wearable systems, which stroke survivors can easily use for long-term activities. APPROACH For stroke applications, FT mainly includes the "flexible/stretchable electronics", "e-textile (electronic textile)" and "soft robotics". Thus, a thorough literature review has been performed to report the practical implementation of FT for post-stroke application. MAIN RESULTS In this review, the highlights of the advancement of FT in stroke rehabilitation systems are dealt with. Such systems mainly involve the "biosignal acquisition unit", "rehabilitation devices" and "assistive systems". In terms of biosignals acquisition, electroencephalography (EEG) and electromyography (EMG) are comprehensively described. For rehabilitation/assistive systems, the application of functional electrical stimulation (FES) and robotics units (exoskeleton, orthosis, etc.) have been explained. SIGNIFICANCE This is the first review article that compiles the different studies regarding flexible technology based post-stroke systems. Furthermore, the technological advantages, limitations, and possible future implications are also discussed to help improve and advance the flexible systems for the betterment of the stroke community.
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Affiliation(s)
- Muhammad Ahmed Khan
- Technical University of Denmark, Ørsteds Plads Building 345C, Room 215, Lyngby, 2800, DENMARK
| | - Matteo Saibene
- Technical University of Denmark, Ørsteds Plads, Building 345C, Lyngby, 2800, DENMARK
| | - Rig Das
- Technical University of Denmark, Ørsteds Plads Building 345C, Room 214, Lyngby, 2800, DENMARK
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Chen X, Lohlein S, Nassour J, Ehrlich SK, Berberich N, Cheng G. Visually-guided Grip Selection for Soft-Hand Exoskeleton. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:4713-4716. [PMID: 34892264 DOI: 10.1109/embc46164.2021.9629982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
This paper presents a visually-guided grip selection based on the combination of object recognition and tactile feedback of a soft-hand exoskeleton intended for hand rehabilitation. A pre-trained neural network is used to recognize the object in front of the hand exoskeleton, which is then mapped to a suitable grip type. With the object cue, it actively assists users in performing different grip movements without calibration. In a pilot experiment, one healthy user completed four different grasp-and-move tasks repeatedly. All trials were completed within 25 seconds and only one out of 20 trials failed. This shows that automated movement training can be achieved by visual guidance even without biomedical sensors. In particular, in the private setting at home without clinical supervision, it is a powerful tool for repetitive training of daily-living activities.
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Palumbo A, Gramigna V, Calabrese B, Ielpo N. Motor-Imagery EEG-Based BCIs in Wheelchair Movement and Control: A Systematic Literature Review. SENSORS (BASEL, SWITZERLAND) 2021; 21:6285. [PMID: 34577493 PMCID: PMC8473300 DOI: 10.3390/s21186285] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Revised: 09/09/2021] [Accepted: 09/14/2021] [Indexed: 02/07/2023]
Abstract
The pandemic emergency of the coronavirus disease 2019 (COVID-19) shed light on the need for innovative aids, devices, and assistive technologies to enable people with severe disabilities to live their daily lives. EEG-based Brain-Computer Interfaces (BCIs) can lead individuals with significant health challenges to improve their independence, facilitate participation in activities, thus enhancing overall well-being and preventing impairments. This systematic review provides state-of-the-art applications of EEG-based BCIs, particularly those using motor-imagery (MI) data, to wheelchair control and movement. It presents a thorough examination of the different studies conducted since 2010, focusing on the algorithm analysis, features extraction, features selection, and classification techniques used as well as on wheelchair components and performance evaluation. The results provided in this paper could highlight the limitations of current biomedical instrumentations applied to people with severe disabilities and bring focus to innovative research topics.
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Affiliation(s)
- Arrigo Palumbo
- Department of Medical and Surgical Sciences, “Magna Græcia” University, 88100 Catanzaro, Italy; (A.P.); (B.C.); (N.I.)
| | - Vera Gramigna
- Neuroscience Research Center, Magna Græcia University, 88100 Catanzaro, Italy
| | - Barbara Calabrese
- Department of Medical and Surgical Sciences, “Magna Græcia” University, 88100 Catanzaro, Italy; (A.P.); (B.C.); (N.I.)
| | - Nicola Ielpo
- Department of Medical and Surgical Sciences, “Magna Græcia” University, 88100 Catanzaro, Italy; (A.P.); (B.C.); (N.I.)
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Aliakbaryhosseinabadi S, Dosen S, Savic AM, Blicher J, Farina D, Mrachacz-Kersting N. Participant-specific classifier tuning increases the performance of hand movement detection from EEG in patients with amyotrophic lateral sclerosis. J Neural Eng 2021; 18. [PMID: 34280899 DOI: 10.1088/1741-2552/ac15e3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 07/19/2021] [Indexed: 11/11/2022]
Abstract
Objective.Brain-computer interface (BCI) systems can be employed to provide motor and communication assistance to patients suffering from neuromuscular diseases, such as amyotrophic lateral sclerosis (ALS). Movement related cortical potentials (MRCPs), which are naturally generated during movement execution, can be used to implement a BCI triggered by motor attempts. Such BCI could assist impaired motor functions of ALS patients during disease progression, and facilitate the training for the generation of reliable MRCPs. The training aspect is relevant to establish a communication channel in the late stage of the disease. Therefore, the aim of this study was to investigate the possibility of detecting MRCPs associated to movement intention in ALS patients with different levels of disease progression from slight to complete paralysis.Approach.Electroencephalography signals were recorded from nine channels in 30 ALS patients at various stages of the disease while they performed or attempted to perform hand movements timed to a visual cue. The movement detection was implemented using offline classification between movement and rest phase. Temporal and spectral features were extracted using 500 ms sliding windows with 50% overlap. The detection was tested for each individual channel and two surrogate channels by performing feature selection followed by classification using linear and non-linear support vector machine and linear discriminant analysis.Main results.The results demonstrated that the detection performance was high in all patients (accuracy 80.5 ± 5.6%) but that the classification parameters (channel, features and classifier) leading to the best performance varied greatly across patients. When the same channel and classifier were used for all patients (participant-generic analysis), the performance significantly decreased (accuracy 74 ± 8.3%).Significance.The present study demonstrates that to maximize the detection of brain waves across ALS patients at different stages of the disease, the classification pipeline should be tuned to each patient individually.
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Affiliation(s)
| | - Strahinja Dosen
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Andrej M Savic
- Science and Research Centre, University of Belgrade-School of Electrical Engineering, Belgrade 11000, Serbia
| | - Jakob Blicher
- Department of Clinical Medicine, Center of Functionally Integrative Neuroscience, Århus University, Aarhus, Denmark
| | - Dario Farina
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Natalie Mrachacz-Kersting
- Department of Sport and Sport Science, Albert-Ludwigs University Freiburg, Freiburg im Breisgau, Germany
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Gaughan TCLS, Boe SG. Investigating the dose-response relationship between motor imagery and motor recovery of upper-limb impairment and function in chronic stroke: A scoping review. J Neuropsychol 2021; 16:54-74. [PMID: 34396708 DOI: 10.1111/jnp.12261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 07/16/2021] [Indexed: 11/28/2022]
Abstract
The recovery of upper-limb impairment and dysfunction post-stroke is often incomplete owing to the limited time in therapy focused on upper-limb recovery and the severity of the impairment. In these cases, motor imagery (MI) may be used as a precursor to physical therapies to initiate rehabilitation early on when it would be otherwise impossible to engage in therapy, as well as to increase the dose of therapy when MI is used in adjunct to physical therapy. While previous reviews have shown MI to be effective as a therapeutic option, disparity in findings exists, with some studies suggesting MI is not an effective treatment for post-stroke impairment and dysfunction. One factor contributing to these findings is inconsistency in the dose of MI applied. To explore the relationship between MI dose and recovery, a scoping review of MI literature as a treatment for adult survivors of stroke with chronic upper-limb motor deficit was performed. Embase, Medline and CINHAL databases were searched for articles related to MI and stroke. Following a two-phase review process, 21 papers were included, and data related to treatment dose and measures of impairment and function were extracted. Effect sizes were calculated to investigate the effect of dosage on motor recovery. Findings showed a high degree of variability in dosage regimens across studies, with no clear pattern for the effect of dose on outcome. The present review highlights the gaps in MI literature, including variables that contribute to the dose-response relationship, that future studies should consider when implementing MI.
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Affiliation(s)
- Theresa C L S Gaughan
- Laboratory for Brain Recovery and Function, Dalhousie University, Halifax, Nova Scotia, Canada.,School of Physiotherapy, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Shaun G Boe
- Laboratory for Brain Recovery and Function, Dalhousie University, Halifax, Nova Scotia, Canada.,School of Physiotherapy, Dalhousie University, Halifax, Nova Scotia, Canada.,Department of Psychology and Neuroscience, Dalhousie University, Halifax, Nova Scotia, Canada.,School of Health and Human Performance, Dalhousie University, Halifax, Nova Scotia, Canada
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Effects of a Soft Robotic Hand for Hand Rehabilitation in Chronic Stroke Survivors. J Stroke Cerebrovasc Dis 2021; 30:105812. [PMID: 33895427 DOI: 10.1016/j.jstrokecerebrovasdis.2021.105812] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 03/11/2021] [Accepted: 04/02/2021] [Indexed: 11/21/2022] Open
Abstract
OBJECTIVES Soft robotic hands are proposed for stroke rehabilitation in terms of their high compliance and low inherent stiffness. We investigated the clinical efficacy of a soft robotic hand that could actively flex and extend the fingers in chronic stroke subjects with different levels of spasticity. METHODS Sixteen chronic stroke subjects were recruited into this single-group study. Subjects underwent 20 sessions of 1-hour EMG-driven soft robotic hand training. Training effect was evaluated by the pre-training and post-training assessments with the clinical scores: Action Research Arm Test(ARAT), Fugl-Meyer Assessment for Upper Extremity(FMA-UE), Box-and-Block test(BBT), Modified Ashworth Scale(MAS), and maximum voluntary grip strength. RESULTS For all the recruited subjects (n = 16), significant improvement of upper limb function was generally observed in ARAT (increased mean=2.44, P = 0.032), FMA-UE (increased mean=3.31, P = 0.003), BBT (increased mean=1.81, P = 0.024), and maximum voluntary grip strength (increased mean=2.14 kg, P < 0.001). No significant change was observed in terms of spasticity with the MAS (decreased mean=0.11, P = 0.423). Further analysis showed subjects with mild or no finger flexor spasticity (MAS<2, n = 9) at pre-training had significant improvement of upper limb function after 20 sessions of training. However, for subjects with moderate and severe finger flexor spasticity (MAS=2,3, n = 7) at pre-training, no significant change in clinical scores was shown and only maximum voluntary grip strength had significant increase. CONCLUSION EMG-driven rehabilitation training using the soft robotic hand with flexion and extension could be effective for the functional recovery of upper limb in chronic stroke subjects with mild or no spasticity.
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