1
|
Tseng KC, Wang L, Hsieh C, Wong AM. Portable robots for upper-limb rehabilitation after stroke: a systematic review and meta-analysis. Ann Med 2024; 56:2337735. [PMID: 38640459 PMCID: PMC11034452 DOI: 10.1080/07853890.2024.2337735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 02/28/2024] [Indexed: 04/21/2024] Open
Abstract
BACKGROUND Robot-assisted upper-limb rehabilitation has been studied for many years, with many randomised controlled trials (RCTs) investigating the effects of robotic-assisted training on affected limbs. The current trend directs towards end-effector devices. However, most studies have focused on the effectiveness of rehabilitation devices, but studies on device sizes are relatively few. GOAL Systematically review the effect of a portable rehabilitation robot (PRR) on the rehabilitation effectiveness of paralysed upper limbs compared with non-robotic therapy. METHODS A meta-analysis was conducted on literature that included the Fugl-Meyer Assessment (FMA) obtained from the PubMed and Web of Science (WoS) electronic databases until June 2023. RESULTS A total of 9 studies, which included RCTs, were completed and a meta-analysis was conducted on 8 of them. The analysis involved 295 patients. The influence on upper-limb function before and after treatment in a clinical environment is analysed by comparing the experimental group using the portable upper-limb rehabilitation robot with the control group using conventional therapy. The result shows that portable robots prove to be effective (FMA: SMD = 0.696, 95% = 0.099 to.293, p < 0.05). DISCUSSION Both robot-assisted and conventional rehabilitation effects are comparable. In some studies, PRR performs better than conventional rehabilitation, but conventional treatments are still irreplaceable. Smaller size with better portability has its advantages, and portable upper-limb rehabilitation robots are feasible in clinical rehabilitation. CONCLUSION Although portable upper-limb rehabilitation robots are clinically beneficial, few studies have focused on portability. Further research should focus on modular design so that rehabilitation robots can be decomposed, which benefits remote rehabilitation and household applications.
Collapse
Affiliation(s)
- Kevin C. Tseng
- Department of Industrial Design, National Taipei University of Technology, Taipei, Taiwan, ROC
- Product Design and Development Laboratory, Taoyuan, Taiwan, ROC
| | - Le Wang
- Product Design and Development Laboratory, Taoyuan, Taiwan, ROC
| | - Chunkai Hsieh
- Product Design and Development Laboratory, Taoyuan, Taiwan, ROC
| | - Alice M. Wong
- Product Design and Development Laboratory, Taoyuan, Taiwan, ROC
- Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital at Taoyuan, Taoyuan, Taiwan, ROC
| |
Collapse
|
2
|
Kottink AIR, Nikamp CDM, Bos FP, Sluis CKVD, Broek MVD, Onneweer B, Stolwijk-Swüste JM, Brink SM, Voet NBM, Rietman JS, Prange-Lasonder GB. Therapy effect on hand function after home use of a wearable assistive soft-robotic glove supporting grip strength. PLoS One 2024; 19:e0306713. [PMID: 38990858 PMCID: PMC11239026 DOI: 10.1371/journal.pone.0306713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 06/20/2024] [Indexed: 07/13/2024] Open
Abstract
BACKGROUND Soft-robotic gloves with an assist-as-needed control have the ability to assist daily activities where needed, while stimulating active and highly functional movements within the user's possibilities. Employment of hand activities with glove support might act as training for unsupported hand function. OBJECTIVE To evaluate the therapeutic effect of a grip-supporting soft-robotic glove as an assistive device at home during daily activities. METHODS This multicentre intervention trial consisted of 3 pre-assessments (averaged if steady state = PRE), one post-assessment (POST), and one follow-up assessment (FU). Participants with chronic hand function limitations were included. Participants used the Carbonhand glove during six weeks in their home environment on their most affected hand. They were free to choose which activities to use the glove with and for how long. The primary outcome measure was grip strength, secondary outcome measures were pinch strength, hand function and glove use time. RESULTS 63 patients with limitations in hand function resulting from various disorders were included. Significant improvements (difference PRE-POST) were found for grip strength (+1.9 kg, CI 0.8 to 3.1; p = 0.002) and hand function, as measured by Jebson-Taylor Hand Function Test (-7.7 s, CI -13.4 to -1.9; p = 0.002) and Action Research Arm Test (+1.0 point, IQR 2.0; p≤0.001). Improvements persisted at FU. Pinch strength improved slightly in all fingers over six-week glove use, however these differences didn't achieve significance. Participants used the soft-robotic glove for a total average of 33.0 hours (SD 35.3), equivalent to 330 min/week (SD 354) or 47 min/day (SD 51). No serious adverse events occurred. CONCLUSION The present findings showed that six weeks use of a grip-supporting soft-robotic glove as an assistive device at home resulted in a therapeutic effect on unsupported grip strength and hand function. The glove use time also showed that this wearable, lightweight glove was able to assist participants with the performance of daily tasks for prolonged periods.
Collapse
Affiliation(s)
- Anke I R Kottink
- Roessingh Research and Development, Enschede, The Netherlands
- Department of Biomechanical Engineering, University of Twente, Enschede, The Netherlands
| | - Corien D M Nikamp
- Roessingh Research and Development, Enschede, The Netherlands
- Department of Biomedical Signals and Systems, University of Twente, Enschede, The Netherlands
| | - Foskea P Bos
- Reade, Center for Rehabilitation and Rheumatology, Amsterdam, The Netherlands
| | - Corry K van der Sluis
- Department of Rehabilitation Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | | | - Bram Onneweer
- Rijndam Rehabilitation, Rotterdam, The Netherlands
- Department of Rehabilitation Medicine, Erasmus Medical Centre, Rotterdam, The Netherlands
| | - Janneke M Stolwijk-Swüste
- De Hoogstraat Rehabilitation, Utrecht, The Netherlands
- Centre of Excellence for Rehabilitation Medicine, Brain Centre Rudolf Magnus, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Sander M Brink
- Department of Rehabilitation Medicine, Isala, Zwolle, The Netherlands
| | - Nicoline B M Voet
- Rehabilitation Centre Klimmendaal, Arnhem, The Netherlands
- Department of Rehabilitation, Donders Institute for Brain, Radboud University Medical Centre, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Johan S Rietman
- Roessingh Research and Development, Enschede, The Netherlands
- Department of Biomechanical Engineering, University of Twente, Enschede, The Netherlands
- Roessingh Centre for Rehabilitation, Enschede, The Netherlands
| | - Gerdienke B Prange-Lasonder
- Roessingh Research and Development, Enschede, The Netherlands
- Department of Biomechanical Engineering, University of Twente, Enschede, The Netherlands
| |
Collapse
|
3
|
Gouret A, Le Bars S, Porssut T, Waszak F, Chokron S. Advancements in brain-computer interfaces for the rehabilitation of unilateral spatial neglect: a concise review. Front Neurosci 2024; 18:1373377. [PMID: 38784094 PMCID: PMC11111994 DOI: 10.3389/fnins.2024.1373377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 04/24/2024] [Indexed: 05/25/2024] Open
Abstract
This short review examines recent advancements in neurotechnologies within the context of managing unilateral spatial neglect (USN), a common condition following stroke. Despite the success of brain-computer interfaces (BCIs) in restoring motor function, there is a notable absence of effective BCI devices for treating cerebral visual impairments, a prevalent consequence of brain lesions that significantly hinders rehabilitation. This review analyzes current non-invasive BCIs and technological solutions dedicated to cognitive rehabilitation, with a focus on visuo-attentional disorders. We emphasize the need for further research into the use of BCIs for managing cognitive impairments and propose a new potential solution for USN rehabilitation, by combining the clinical subtleties of this syndrome with the technological advancements made in the field of neurotechnologies.
Collapse
Affiliation(s)
- Alix Gouret
- Integrative Neuroscience and Cognition Center (INCC), CNRS, Université Paris Cité, Paris, France
- Research and Innovation Department, Capgemini Engineering, Paris, France
| | - Solène Le Bars
- Integrative Neuroscience and Cognition Center (INCC), CNRS, Université Paris Cité, Paris, France
- Research and Innovation Department, Capgemini Engineering, Paris, France
| | - Thibault Porssut
- Research and Innovation Department, Capgemini Engineering, Paris, France
| | - Florian Waszak
- Integrative Neuroscience and Cognition Center (INCC), CNRS, Université Paris Cité, Paris, France
| | - Sylvie Chokron
- Integrative Neuroscience and Cognition Center (INCC), CNRS, Université Paris Cité, Paris, France
- Research and Innovation Department, Capgemini Engineering, Paris, France
| |
Collapse
|
4
|
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.
Collapse
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
| |
Collapse
|
5
|
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.
Collapse
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
| |
Collapse
|
6
|
Mai X, Ai J, Wei Y, Zhu X, Meng J. Phase-Locked Time-Shift Data Augmentation Method for SSVEP Brain-Computer Interfaces. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4096-4105. [PMID: 37815966 DOI: 10.1109/tnsre.2023.3323351] [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: 10/12/2023]
Abstract
Steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) have achieved an information transfer rate (ITR) of over 300 bits/min, but abundant training data is required. The performance of SSVEP algorithms deteriorates greatly under limited data, and the existing time-shift data augmentation method fails to improve it because the phase-locked requirement between training samples is violated. To address this issue, this study proposes a novel augmentation method, namely phase-locked time-shift (PLTS), for SSVEP-BCI. The similarity between epochs at different time moments was evaluated, and a unique time-shift step was calculated for each class to augment additional data epochs in each trial. The results showed that the PLTS significantly improved the classification performance of SSVEP algorithms on the BETA SSVEP datasets. Moreover, under the condition of one calibration block, by slightly prolonging the calibration duration (from 48 s to 51.5 s), the ITR increased from 40.88±4.54 bits/min to 122.61±7.05 bits/min with the PLTS. This study provides a new perspective on augmenting data epochs for training-based SSVEP-BCI, promotes the classification accuracy and ITR under limited training data, and thus facilitates the real-life applications of SSVEP-based brain spellers.
Collapse
|
7
|
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.
Collapse
|
8
|
Jia H, Feng F, Caiafa CF, Duan F, Zhang Y, Sun Z, Sole-Casals J. Multi-Class Classification of Upper Limb Movements With Filter Bank Task-Related Component Analysis. IEEE J Biomed Health Inform 2023; 27:3867-3877. [PMID: 37227915 DOI: 10.1109/jbhi.2023.3278747] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
The classification of limb movements can provide with control commands in non-invasive brain-computer interface. Previous studies on the classification of limb movements have focused on the classification of left/right limbs; however, the classification of different types of upper limb movements has often been ignored despite that it provides more active-evoked control commands in the brain-computer interface. Nevertheless, few machine learning method can be used as the state-of-the-art method in the multi-class classification of limb movements. This work focuses on the multi-class classification of upper limb movements and proposes the multi-class filter bank task-related component analysis (mFBTRCA) method, which consists of three steps: spatial filtering, similarity measuring and filter bank selection. The spatial filter, namely the task-related component analysis, is first used to remove noise from EEG signals. The canonical correlation measures the similarity of the spatial-filtered signals and is used for feature extraction. The correlation features are extracted from multiple low-frequency filter banks. The minimum-redundancy maximum-relevance selects the essential features from all the correlation features, and finally, the support vector machine is used to classify the selected features. The proposed method compared against previously used models is evaluated using two datasets. mFBTRCA achieved a classification accuracy of 0.4193 ± 0.0780 (7 classes) and 0.4032 ± 0.0714 (5 classes), respectively, which improves on the best accuracies achieved using the compared methods (0.3590 ± 0.0645 and 0.3159 ± 0.0736, respectively). The proposed method is expected to provide more control commands in the applications of non-invasive brain-computer interfaces.
Collapse
|
9
|
Sarhan SM, Al-Faiz MZ, Takhakh AM. A review on EMG/EEG based control scheme of upper limb rehabilitation robots for stroke patients. Heliyon 2023; 9:e18308. [PMID: 37533980 PMCID: PMC10391943 DOI: 10.1016/j.heliyon.2023.e18308] [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: 12/16/2022] [Revised: 07/03/2023] [Accepted: 07/13/2023] [Indexed: 08/04/2023] Open
Abstract
Stroke is a common worldwide health problem and a crucial contributor to gained disability. The abilities of people, who are subjected to stroke, to live independently are significantly affected since affected upper limbs' functions are essential for our daily life. This review article focuses on emerging trends in BCI-controlled rehabilitation techniques based on EMG, EEG, or EGM + EEG signals in the last few years. Working on developing rehabilitation robotics, is considered a wealthy scientific area for researchers in the last period. There is a significant advantage that the human acquires from the interaction between the machine and his body, rehabilitation for a patient's limb is very important to get the body limb recovery, and this is what is provided mostly by applying robotic devices.
Collapse
Affiliation(s)
- Saad M. Sarhan
- Department of Biomedical Engineering, College of Engineering, Al-Nahrain University, Baghdad, Iraq
| | - Mohammed Z. Al-Faiz
- Department of Control and Computer, College of Information Engineering, Al-Nahrain University, Baghdad, Iraq
| | - Ayad M. Takhakh
- Department of Biomechanics, College of Engineering, Al-Nahrain University, Baghdad, Iraq
| |
Collapse
|
10
|
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.
Collapse
|
11
|
Jain A, Kumar L. EEG Cortical Source Feature based Hand Kinematics Decoding using Residual CNN-LSTM Neural Network. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082886 DOI: 10.1109/embc40787.2023.10341052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Motor kinematics decoding (MKD) using brain signal is essential to develop Brain-computer interface (BCI) system for rehabilitation or prosthesis devices. Surface electroencephalogram (EEG) signal has been widely utilized for MKD. However, kinematic decoding from cortical sources is sparsely explored. In this work, the feasibility of hand kinematics decoding using EEG cortical source signals has been explored for grasp and lift task. In particular, pre-movement EEG segment is utilized. A residual convolutional neural network (CNN) - long short-term memory (LSTM) based kinematics decoding model is proposed that utilizes motor neural information present in pre-movement brain activity. Various EEG windows at 50 ms prior to movement onset, are utilized for hand kinematics decoding. Correlation value (CV) between actual and predicted hand kinematics is utilized as performance metric for source and sensor domain. The performance of the proposed deep learning model is compared in sensor and source domain. The results demonstrate the viability of hand kinematics decoding using pre-movement EEG cortical source data.
Collapse
|
12
|
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.
Collapse
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
| |
Collapse
|
13
|
Ko MJ, Chuang YC, Ou-Yang LJ, Cheng YY, Tsai YL, Lee YC. The Application of Soft Robotic Gloves in Stroke Patients: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Brain Sci 2023; 13:900. [PMID: 37371378 DOI: 10.3390/brainsci13060900] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 05/30/2023] [Accepted: 05/31/2023] [Indexed: 06/29/2023] Open
Abstract
Wearable robotic devices have been strongly put into use in both the clinical and research fields of stroke rehabilitation over the past decades. This study aimed to explore the effectiveness of soft robotic gloves (SRGs) towards improving the motor recovery and functional abilities in patients with post-stroke hemiparesis. Five major bibliographic databases, PubMed, Embase, Cochrane Library, Web of Science, and the Physiotherapy Evidence Database, were all reviewed for enrollment regarding comparative trials prior to 7 March 2023. We included adults with stroke and compared their rehabilitation using SRGs to conventional rehabilitation (CR) on hand function in terms of the Fugl-Meyer Upper Extremity Motor Assessment (FMA-UE), Fugl-Meyer Distal Upper Extremity Motor Assessment (FMA-distal UE), box and blocks test score, grip strength test, and the Jebsen-Taylor hand function test (JTT). A total of 8 studies, comprising 309 participants, were included in the analysis. Compared to CR, rehabilitation involving SRGs achieved better FMA-UE (MD 6.52, 95% CI: 3.65~9.39), FMA-distal UE (MD 3.27, 95% CI: 1.50~5.04), and JJT (MD 13.34, CI: 5.16~21.53) results. Subgroup analysis showed that stroke latency of more than 6 months and training for more than 30 min offered a better effect as well. In conclusion, for patients with stroke, rehabilitation using SRGs is recommended to promote the functional abilities of the upper extremities.
Collapse
Affiliation(s)
- Ming-Jian Ko
- Department of Education, Taichung Veterans General Hospital, Taichung 407219, Taiwan
| | - Ya-Chi Chuang
- Department of Physical Medicine and Rehabilitation, Taichung Veterans General Hospital, Taichung 407219, Taiwan
| | - Liang-Jun Ou-Yang
- Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital, Linkou, Taoyuan 333423, Taiwan
| | - Yuan-Yang Cheng
- Department of Physical Medicine and Rehabilitation, Taichung Veterans General Hospital, Taichung 407219, Taiwan
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung 402202, Taiwan
| | - Yu-Lin Tsai
- Department of Physical Medicine and Rehabilitation, Taichung Veterans General Hospital, Taichung 407219, Taiwan
| | - Yu-Chun Lee
- Department of Physical Medicine and Rehabilitation, Taichung Veterans General Hospital, Taichung 407219, Taiwan
- Department of Exercise Health Science, National Taiwan University of Sport, Taichung 404401, Taiwan
- Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung 407224, Taiwan
| |
Collapse
|