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Li J, She Q, Meng M, Du S, Zhang Y. Three-stage transfer learning for motor imagery EEG recognition. Med Biol Eng Comput 2024; 62:1689-1701. [PMID: 38342784 DOI: 10.1007/s11517-024-03036-9] [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: 05/18/2023] [Accepted: 01/17/2024] [Indexed: 02/13/2024]
Abstract
Motor imagery (MI) paradigms have been widely used in neural rehabilitation and drowsiness state assessment. The progress in brain-computer interface (BCI) technology has emphasized the importance of accurately and efficiently detecting motor imagery intentions from electroencephalogram (EEG). Despite the recent breakthroughs made in developing EEG-based algorithms for decoding MI, the accuracy and efficiency of these models remain limited by technical challenges posed by cross-subject heterogeneity in EEG data processing and the scarcity of EEG data for training. Inspired by the optimal transport theory, this study aims to develop a novel three-stage transfer learning (TSTL) method, which uses the existing labeled data from a source domain to improve classification performance on an unlabeled target domain. Notably, the proposed method comprises three components, namely, the Riemannian tangent space mapping (RTSM), source domain transformer (SDT), and optimal subspace mapping (OSM). The RTSM maps a symmetric positive definite matrix from the Riemannian space to the tangent space to minimize the marginal probability distribution drift. The SDT transforms the source domain to a target domain by finding the optimal transport mapping matrix to reduce the joint probability distribution differences. The OSM finally maps the transformed source domain and original target domain to the same subspace to further mitigate the distribution discrepancy. The performance of the proposed method was validated on two public BCI datasets, and the average accuracy of the algorithm on two datasets was 72.24% and 69.29%. Our results demonstrated the improved performance of EEG-based MI detection in comparison with state-of-the-art algorithms.
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Affiliation(s)
- Junhao Li
- School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
| | - Qingshan She
- School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China.
| | - Ming Meng
- School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
| | - Shengzhi Du
- Department of Electrical Engineering, Tshwane University of Technology, Pretoria, 0001, South Africa
| | - Yingchun Zhang
- Department of Biomedical Engineering, University of Houston, Houston, TX, 77204, USA
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Khan MA, Fares H, Ghayvat H, Brunner IC, Puthusserypady S, Razavi B, Lansberg M, Poon A, Meador KJ. A systematic review on functional electrical stimulation based rehabilitation systems for upper limb post-stroke recovery. Front Neurol 2023; 14:1272992. [PMID: 38145118 PMCID: PMC10739305 DOI: 10.3389/fneur.2023.1272992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 11/20/2023] [Indexed: 12/26/2023] Open
Abstract
Background Stroke is one of the most common neurological conditions that often leads to upper limb motor impairments, significantly affecting individuals' quality of life. Rehabilitation strategies are crucial in facilitating post-stroke recovery and improving functional independence. Functional Electrical Stimulation (FES) systems have emerged as promising upper limb rehabilitation tools, offering innovative neuromuscular reeducation approaches. Objective The main objective of this paper is to provide a comprehensive systematic review of the start-of-the-art functional electrical stimulation (FES) systems for upper limb neurorehabilitation in post-stroke therapy. More specifically, this paper aims to review different types of FES systems, their feasibility testing, or randomized control trials (RCT) studies. Methods The FES systems classification is based on the involvement of patient feedback within the FES control, which mainly includes "Open-Loop FES Systems" (manually controlled) and "Closed-Loop FES Systems" (brain-computer interface-BCI and electromyography-EMG controlled). Thus, valuable insights are presented into the technological advantages and effectiveness of Manual FES, EEG-FES, and EMG-FES systems. Results and discussion The review analyzed 25 studies and found that the use of FES-based rehabilitation systems resulted in favorable outcomes for the stroke recovery of upper limb functional movements, as measured by the FMA (Fugl-Meyer Assessment) (Manually controlled FES: mean difference = 5.6, 95% CI (3.77, 7.5), P < 0.001; BCI-controlled FES: mean difference = 5.37, 95% CI (4.2, 6.6), P < 0.001; EMG-controlled FES: mean difference = 14.14, 95% CI (11.72, 16.6), P < 0.001) and ARAT (Action Research Arm Test) (EMG-controlled FES: mean difference = 11.9, 95% CI (8.8, 14.9), P < 0.001) scores. Furthermore, the shortcomings, clinical considerations, comparison to non-FES systems, design improvements, and possible future implications are also discussed for improving stroke rehabilitation systems and advancing post-stroke recovery. Thus, summarizing the existing literature, this review paper can help researchers identify areas for further investigation. This can lead to formulating research questions and developing new studies aimed at improving FES systems and their outcomes in upper limb rehabilitation.
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Affiliation(s)
- Muhammad Ahmed Khan
- Department of Neurology and Neurological Sciences, Stanford University, Palo Alto, CA, United States
- Department of Electrical Engineering, Stanford University, Palo Alto, CA, United States
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Hoda Fares
- Department of Electrical, Electronic, Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa, Genoa, Italy
| | - Hemant Ghayvat
- Department of Computer Science, Linnaeus University, Växjö, Sweden
| | | | | | - Babak Razavi
- Department of Neurology and Neurological Sciences, Stanford University, Palo Alto, CA, United States
| | - Maarten Lansberg
- Department of Neurology and Neurological Sciences, Stanford University, Palo Alto, CA, United States
| | - Ada Poon
- Department of Electrical Engineering, Stanford University, Palo Alto, CA, United States
| | - Kimford Jay Meador
- Department of Neurology and Neurological Sciences, Stanford University, Palo Alto, CA, United States
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Zhang D, Chen Z, Xiao L, Zhu B, Wu R, Ou C, Ma Y, Xie L, Jiang H. Stretchable and durable HD-sEMG electrodes for accurate recognition of swallowing activities on complex epidermal surfaces. MICROSYSTEMS & NANOENGINEERING 2023; 9:115. [PMID: 37731914 PMCID: PMC10507084 DOI: 10.1038/s41378-023-00591-3] [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: 05/03/2023] [Revised: 07/19/2023] [Accepted: 08/09/2023] [Indexed: 09/22/2023]
Abstract
Surface electromyography (sEMG) is widely used in monitoring human health. Nonetheless, it is challenging to capture high-fidelity sEMG recordings in regions with intricate curved surfaces such as the larynx, because regular sEMG electrodes have stiff structures. In this study, we developed a stretchable, high-density sEMG electrode array via layer-by-layer printing and lamination. The electrode offered a series of excellent human‒machine interface features, including conformal adhesion to the skin, high electron-to-ion conductivity (and thus lower contact impedance), prolonged environmental adaptability to resist water evaporation, and epidermal biocompatibility. This made the electrode more appropriate than commercial electrodes for long-term wearable, high-fidelity sEMG recording devices at complicated skin interfaces. Systematic in vivo studies were used to investigate its ability to classify swallowing activities, which was accomplished with high accuracy by decoding the sEMG signals from the chin via integration with an ear-mounted wearable system and machine learning algorithms. The results demonstrated the clinical feasibility of the system for noninvasive and comfortable recognition of swallowing motions for comfortable dysphagia rehabilitation.
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Affiliation(s)
- Ding Zhang
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 511442 P. R. China
| | - Zhitao Chen
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 511442 P. R. China
| | - Longya Xiao
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 511442 P. R. China
| | - Beichen Zhu
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 511442 P. R. China
| | - RuoXuan Wu
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou, 511442 P. R. China
| | - ChengJian Ou
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 511442 P. R. China
| | - Yi Ma
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 511442 P. R. China
| | - Longhan Xie
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 511442 P. R. China
| | - Hongjie Jiang
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 511442 P. R. China
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Choy CS, Fang Q, Neville K, Ding B, Kumar A, Mahmoud SS, Gu X, Fu J, Jelfs B. Virtual reality and motor imagery for early post-stroke rehabilitation. Biomed Eng Online 2023; 22:66. [PMID: 37407988 PMCID: PMC10320905 DOI: 10.1186/s12938-023-01124-9] [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: 11/20/2022] [Accepted: 06/05/2023] [Indexed: 07/07/2023] Open
Abstract
BACKGROUND Motor impairment is a common consequence of stroke causing difficulty in independent movement. The first month of post-stroke rehabilitation is the most effective period for recovery. Movement imagination, known as motor imagery, in combination with virtual reality may provide a way for stroke patients with severe motor disabilities to begin rehabilitation. METHODS The aim of this study is to verify whether motor imagery and virtual reality help to activate stroke patients' motor cortex. 16 acute/subacute (< 6 months) stroke patients participated in this study. All participants performed motor imagery of basketball shooting which involved the following tasks: listening to audio instruction only, watching a basketball shooting animation in 3D with audio, and also performing motor imagery afterwards. Electroencephalogram (EEG) was recorded for analysis of motor-related features of the brain such as power spectral analysis in the [Formula: see text] and [Formula: see text] frequency bands and spectral entropy. 18 EEG channels over the motor cortex were used for all stroke patients. RESULTS All results are normalised relative to all tasks for each participant. The power spectral densities peak near the [Formula: see text] band for all participants and also the [Formula: see text] band for some participants. Tasks with instructions during motor imagery generally show greater power spectral peaks. The p-values of the Wilcoxon signed-rank test for band power comparison from the 18 EEG channels between different pairs of tasks show a 0.01 significance of rejecting the band powers being the same for most tasks done by stroke subjects. The motor cortex of most stroke patients is more active when virtual reality is involved during motor imagery as indicated by their respective scalp maps of band power and spectral entropy. CONCLUSION The resulting activation of stroke patient's motor cortices in this study reveals evidence that it is induced by imagination of movement and virtual reality supports motor imagery. The framework of the current study also provides an efficient way to investigate motor imagery and virtual reality during post-stroke rehabilitation.
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Affiliation(s)
- Chi S. Choy
- School of Engineering, RMIT University, Melbourne, Australia
| | - Qiang Fang
- Department of Biomedical Engineering, Shantou University, Shantou, China
| | - Katrina Neville
- School of Engineering, RMIT University, Melbourne, Australia
| | - Bingrui Ding
- Department of Biomedical Engineering, Shantou University, Shantou, China
| | - Akshay Kumar
- Department of Biomedical Engineering, Shantou University, Shantou, China
| | | | - Xudong Gu
- Rehabilitation Center, Jiaxing 2nd Hospital, Jiaxing, 314000 China
| | - Jianming Fu
- Rehabilitation Center, Jiaxing 2nd Hospital, Jiaxing, 314000 China
| | - Beth Jelfs
- Department of Electrical, Electronic & Systems Engineering, University of Birmingham, Birmingham, UK
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Vélez-Guerrero MA, Callejas-Cuervo M, Álvarez JC, Mazzoleni S. Assessment of the Mechanical Support Characteristics of a Light and Wearable Robotic Exoskeleton Prototype Applied to Upper Limb Rehabilitation. SENSORS (BASEL, SWITZERLAND) 2022; 22:3999. [PMID: 35684618 PMCID: PMC9185240 DOI: 10.3390/s22113999] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 05/16/2022] [Accepted: 05/18/2022] [Indexed: 02/06/2023]
Abstract
Robotic exoskeletons are active devices that assist or counteract the movements of the body limbs in a variety of tasks, including in industrial environments or rehabilitation processes. With the introduction of textile and soft materials in these devices, the effective motion transmission, mechanical support of the limbs, and resistance to physical disturbances are some of the most desirable structural features. This paper proposes an evaluation protocol and assesses the mechanical support properties of a servo-controlled robotic exoskeleton prototype for rehabilitation in upper limbs. Since this prototype was built from soft materials, it is necessary to evaluate the mechanical behavior in the areas that support the arm. Some of the rehabilitation-supporting movements such as elbow flexion and extension, as well as increased muscle tone (spasticity), are emulated. Measurements are taken using the reference supplied to the system's control stage and then compared with an external high-precision optical tracking system. As a result, it is evidenced that the use of soft materials provides satisfactory outcomes in the motion transfer and support to the limb. In addition, this study lays the groundwork for a future assessment of the prototype in a controlled laboratory environment using human test subjects.
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Affiliation(s)
| | - Mauro Callejas-Cuervo
- Software Research Group, Universidad Pedagógica y Tecnológica de Colombia, Tunja 150002, Colombia;
| | - Juan C. Álvarez
- Multisensor Systems and Robotics Group (SiMuR), Department of Electrical, Electronic, Computer and Systems Engineering, University of Oviedo, C/Pedro Puig Adam, 33203 Gijón, Spain;
| | - Stefano Mazzoleni
- Department of Electrical and Information Engineering, Polytechnic University of Bari, 70126 Bari, Italy;
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