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Zhang D, Li H, Xie J. Unsupervised and semi-supervised domain adaptation networks considering both global knowledge and prototype-based local class information for Motor Imagery Classification. Neural Netw 2024; 179:106497. [PMID: 38986186 DOI: 10.1016/j.neunet.2024.106497] [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: 11/10/2023] [Revised: 05/21/2024] [Accepted: 06/25/2024] [Indexed: 07/12/2024]
Abstract
The non-stationarity of EEG signals results in variability across sessions, impeding model building and data sharing. In this paper, we propose a domain adaptation method called GPL, which simultaneously considers global knowledge and prototype-based local class information to enhance the classification accuracy of motor imagery signals. Depending on the amount of labeled data available in the target domain, the method is implemented in both unsupervised and semi-supervised versions. Specifically, at the global level, we employ the maximum mean difference (MMD) loss to globally constrain the feature space, achieving comprehensive alignment. In the context of class-level operations, we propose two memory banks designed to accommodate class prototypes in each domain and constrain feature embeddings by applying two prototype-based contrastive losses. The source contrastive loss is used to organize source features spatially based on categories, thereby reconciling inter-class and intra-class relationships, while the interactive contrastive loss is employed to facilitate cross-domain information interaction. Simultaneously, in unsupervised scenarios, to mitigate the adverse effects of excessive pseudo-labels, we introduce an entropy-aware strategy that dynamically evaluates the confidence level of target data and personalized constraints on the participation of interactive contrastive loss. To validate our approach, extensive experiments were conducted on a highly regarded public EEG dataset, namely Dataset IIa of the BCI Competition IV, as well as a large-scale EEG dataset called GigaDB. The experiments yielded average classification accuracies of 86.03% and 84.22% respectively. These results demonstrate that our method is an effective EEG decoding model, conducive to advancing the development of motor imagery brain-computer interfaces. The architecture proposed in this study and the code for data partitioning can be found at https://github.com/zhangdx21/GPL.
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Affiliation(s)
- Dongxue Zhang
- Jilin University, College of Computer Science and Technology, Changchun, Jilin Province, China; Key Laboratory of Symbol Computation and Knowledge Engineering, Jilin University, Changchun 130012, China
| | - Huiying Li
- Jilin University, College of Computer Science and Technology, Changchun, Jilin Province, China; Key Laboratory of Symbol Computation and Knowledge Engineering, Jilin University, Changchun 130012, China.
| | - Jingmeng Xie
- Xi'an Jiaotong University, College of Electronic information, Xi'an, Shanxi Province, China
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Zhang H, Jiao L, Yang S, Li H, Jiang X, Feng J, Zou S, Xu Q, Gu J, Wang X, Wei B. Brain-computer interfaces: the innovative key to unlocking neurological conditions. Int J Surg 2024; 110:5745-5762. [PMID: 39166947 PMCID: PMC11392146 DOI: 10.1097/js9.0000000000002022] [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: 05/23/2024] [Accepted: 07/30/2024] [Indexed: 08/23/2024]
Abstract
Neurological disorders such as Parkinson's disease, stroke, and spinal cord injury can pose significant threats to human mortality, morbidity, and functional independence. Brain-Computer Interface (BCI) technology, which facilitates direct communication between the brain and external devices, emerges as an innovative key to unlocking neurological conditions, demonstrating significant promise in this context. This comprehensive review uniquely synthesizes the latest advancements in BCI research across multiple neurological disorders, offering an interdisciplinary perspective on both clinical applications and emerging technologies. We explore the progress in BCI research and its applications in addressing various neurological conditions, with a particular focus on recent clinical studies and prospective developments. Initially, the review provides an up-to-date overview of BCI technology, encompassing its classification, operational principles, and prevalent paradigms. It then critically examines specific BCI applications in movement disorders, disorders of consciousness, cognitive and mental disorders, as well as sensory disorders, highlighting novel approaches and their potential impact on patient care. This review reveals emerging trends in BCI applications, such as the integration of artificial intelligence and the development of closed-loop systems, which represent significant advancements over previous technologies. The review concludes by discussing the prospects and directions of BCI technology, underscoring the need for interdisciplinary collaboration and ethical considerations. It emphasizes the importance of prioritizing bidirectional and high-performance BCIs, areas that have been underexplored in previous reviews. Additionally, we identify crucial gaps in current research, particularly in long-term clinical efficacy and the need for standardized protocols. The role of neurosurgery in spearheading the clinical translation of BCI research is highlighted. Our comprehensive analysis presents BCI technology as an innovative key to unlocking neurological disorders, offering a transformative approach to diagnosing, treating, and rehabilitating neurological conditions, with substantial potential to enhance patients' quality of life and advance the field of neurotechnology.
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Affiliation(s)
- Hongyu Zhang
- Department of Neurosurgery, The Fourth Affiliated Hospital of Harbin Medical University
- Harbin Medical University, Harbin
| | - Le Jiao
- Department of Neurosurgery, The First Hospital of Qiqihar, Qiqihar, Heilongjiang Province
| | | | | | | | - Jing Feng
- Department of Neurosurgery, The Fourth Affiliated Hospital of Harbin Medical University
- Harbin Medical University, Harbin
| | - Shuhuai Zou
- Department of Neurosurgery, The Fourth Affiliated Hospital of Harbin Medical University
- Harbin Medical University, Harbin
| | - Qiang Xu
- Department of Neurosurgery, The Fourth Affiliated Hospital of Harbin Medical University
- Harbin Medical University, Harbin
| | - Jianheng Gu
- Department of Neurosurgery, The Fourth Affiliated Hospital of Harbin Medical University
- Harbin Medical University, Harbin
| | - Xuefeng Wang
- Department of Neurosurgery, The Fourth Affiliated Hospital of Harbin Medical University
| | - Baojian Wei
- School of Nursing, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, Shandong, People's Republic of China
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Zhang Z, Xu P, Wu C, Yu H. Smart Nursing Wheelchairs: A New Trend in Assisted Care and the Future of Multifunctional Integration. Biomimetics (Basel) 2024; 9:492. [PMID: 39194471 DOI: 10.3390/biomimetics9080492] [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: 06/25/2024] [Revised: 08/06/2024] [Accepted: 08/12/2024] [Indexed: 08/29/2024] Open
Abstract
As a significant technological innovation in the fields of medicine and geriatric care, smart care wheelchairs offer a novel approach to providing high-quality care services and improving the quality of care. The aim of this review article is to examine the development, applications and prospects of smart nursing wheelchairs, with particular emphasis on their assistive nursing functions, multiple-sensor fusion technology, and human-machine interaction interfaces. First, we describe the assistive functions of nursing wheelchairs, including position changing, transferring, bathing, and toileting, which significantly reduce the workload of nursing staff and improve the quality of care. Second, we summarized the existing multiple-sensor fusion technology for smart nursing wheelchairs, including LiDAR, RGB-D, ultrasonic sensors, etc. These technologies give wheelchairs autonomy and safety, better meeting patients' needs. We also discussed the human-machine interaction interfaces of intelligent care wheelchairs, such as voice recognition, touch screens, and remote controls. These interfaces allow users to operate and control the wheelchair more easily, improving usability and maneuverability. Finally, we emphasized the importance of multifunctional-integrated care wheelchairs that integrate assistive care, navigation, and human-machine interaction functions into a comprehensive care solution for users. We are looking forward to the future and assume that smart nursing wheelchairs will play an increasingly important role in medicine and geriatric care. By integrating advanced technologies such as enhanced artificial intelligence, intelligent sensors, and remote monitoring, we expect to further improve patients' quality of care and quality of life.
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Affiliation(s)
- Zhewen Zhang
- Rehabilitation Engineering and Technology Institute, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Peng Xu
- Rehabilitation Engineering and Technology Institute, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Chengjia Wu
- Rehabilitation Engineering and Technology Institute, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Hongliu Yu
- Rehabilitation Engineering and Technology Institute, University of Shanghai for Science and Technology, Shanghai 200093, China
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Kosmyna N, Hauptmann E, Hmaidan Y. A Brain-Controlled Quadruped Robot: A Proof-of-Concept Demonstration. SENSORS (BASEL, SWITZERLAND) 2023; 24:80. [PMID: 38202942 PMCID: PMC10780665 DOI: 10.3390/s24010080] [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: 10/16/2023] [Revised: 12/15/2023] [Accepted: 12/19/2023] [Indexed: 01/12/2024]
Abstract
Coupling brain-computer interfaces (BCIs) and robotic systems in the future can enable seamless personal assistant systems in everyday life, with the requests that can be performed in a discrete manner, using one's brain activity only. These types of systems might be of a particular interest for people with locked-in syndrome (LIS) or amyotrophic lateral sclerosis (ALS) because they can benefit from communicating with robotic assistants using brain sensing interfaces. In this proof-of-concept work, we explored how a wireless and wearable BCI device can control a quadruped robot-Boston Dynamics' Spot. The device measures the user's electroencephalography (EEG) and electrooculography (EOG) activity of the user from the electrodes embedded in the glasses' frame. The user responds to a series of questions with YES/NO answers by performing a brain-teaser activity of mental calculus. Each question-answer pair has a pre-configured set of actions for Spot. For instance, Spot was prompted to walk across a room, pick up an object, and retrieve it for the user (i.e., bring a bottle of water) when a sequence resolved to a YES response. Our system achieved at a success rate of 83.4%. To the best of our knowledge, this is the first integration of wireless, non-visual-based BCI systems with Spot in the context of personal assistant use cases. While this BCI quadruped robot system is an early prototype, future iterations may embody friendly and intuitive cues similar to regular service dogs. As such, this project aims to pave a path towards future developments in modern day personal assistant robots powered by wireless and wearable BCI systems in everyday living conditions.
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Affiliation(s)
- Nataliya Kosmyna
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | | | - Yasmeen Hmaidan
- Psychology Department, University of Toronto, Toronto, ON M5S 3E4, Canada;
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Sumner J, Lim HW, Chong LS, Bundele A, Mukhopadhyay A, Kayambu G. Artificial intelligence in physical rehabilitation: A systematic review. Artif Intell Med 2023; 146:102693. [PMID: 38042593 DOI: 10.1016/j.artmed.2023.102693] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 10/25/2023] [Accepted: 10/29/2023] [Indexed: 12/04/2023]
Abstract
BACKGROUND Physical disabilities become more common with advancing age. Rehabilitation restores function, maintaining independence for longer. However, the poor availability and accessibility of rehabilitation limits its clinical impact. Artificial Intelligence (AI) guided interventions have improved many domains of healthcare, but whether rehabilitation can benefit from AI remains unclear. METHODS We conducted a systematic review of AI-supported physical rehabilitation technology tested in the clinical setting to understand: 1) availability of AI-supported physical rehabilitation technology; 2) its clinical effect; 3) and the barriers and facilitators to implementation. We searched in MEDLINE, EMBASE, CINAHL, Science Citation Index (Web of Science), CIRRIE (now NARIC), and OpenGrey. RESULTS We identified 9054 articles and included 28 projects. AI solutions spanned five categories: App-based systems, robotic devices that replace function, robotic devices that restore function, gaming systems and wearables. We identified five randomised controlled trials (RCTs), which evaluated outcomes relating to physical function, activity, pain, and health-related quality of life. The clinical effects were inconsistent. Implementation barriers included technology literacy, reliability, and user fatigue. Enablers included greater access to rehabilitation programmes, remote monitoring of progress, reduction in manpower requirements and lower cost. CONCLUSION Application of AI in physical rehabilitation is a growing field, but clinical effects have yet to be studied rigorously. Developers must strive to conduct robust clinical evaluations in the real-world setting and appraise post implementation experiences.
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Affiliation(s)
- Jennifer Sumner
- Medical Affairs - Research Innovation & Enterprise, Alexandra Hospital, National University Health System, Singapore.
| | - Hui Wen Lim
- Medical Affairs - Research Innovation & Enterprise, Alexandra Hospital, National University Health System, Singapore
| | - Lin Siew Chong
- Medical Affairs - Research Innovation & Enterprise, Alexandra Hospital, National University Health System, Singapore
| | - Anjali Bundele
- Medical Affairs - Research Innovation & Enterprise, Alexandra Hospital, National University Health System, Singapore
| | - Amartya Mukhopadhyay
- Yong Loo Lin School of Medicine, Department of Medicine, National University of Singapore, Singapore; Medical Affairs - Research Innovation & Enterprise, Alexandra Hospital, National University Health System, Singapore; Division of Respiratory and Critical Care Medicine, Department of Medicine, National University Hospital, Singapore
| | - Geetha Kayambu
- Department of Rehabilitation, National University Hospital, Singapore
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Sun H, Jin J, Daly I, Huang Y, Zhao X, Wang X, Cichocki A. Feature learning framework based on EEG graph self-attention networks for motor imagery BCI systems. J Neurosci Methods 2023; 399:109969. [PMID: 37683772 DOI: 10.1016/j.jneumeth.2023.109969] [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: 05/26/2023] [Revised: 08/18/2023] [Accepted: 09/03/2023] [Indexed: 09/10/2023]
Abstract
Learning distinguishable features from raw EEG signals is crucial for accurate classification of motor imagery (MI) tasks. To incorporate spatial relationships between EEG sources, we developed a feature set based on an EEG graph. In this graph, EEG channels represent the nodes, with power spectral density (PSD) features defining their properties, and the edges preserving the spatial information. We designed an EEG based graph self-attention network (EGSAN) to learn low-dimensional embedding vector for EEG graph, which can be used as distinguishable features for motor imagery task classification. We evaluated our EGSAN model on two publicly available MI EEG datasets, each containing different types of motor imagery tasks. Our experiments demonstrate that our proposed model effectively extracts distinguishable features from EEG graphs, achieving significantly higher classification accuracies than existing state-of-the-art methods.
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Affiliation(s)
- Hao Sun
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Jing Jin
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China; Shenzhen Research Institute of East China University of Science and Technology, Shen Zhen 518063, China.
| | - Ian Daly
- Brain-Computer Interfacing and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, United Kingdom
| | - Yitao Huang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Xueqing Zhao
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Xingyu Wang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Andrzej Cichocki
- RIKEN Brain Science Institute, Wako 351-0198, Japan; Nicolaus Copernicus University (UMK), 87-100 Torun, Poland
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Cisek K, Kelleher JD. Current Topics in Technology-Enabled Stroke Rehabilitation and Reintegration: A Scoping Review and Content Analysis. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3341-3352. [PMID: 37578924 DOI: 10.1109/tnsre.2023.3304758] [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: 08/16/2023]
Abstract
BACKGROUND There is a worldwide health crisis stemming from the rising incidence of various debilitating chronic diseases, with stroke as a leading contributor. Chronic stroke management encompasses rehabilitation and reintegration, and can require decades of personalized medicine and care. Information technology (IT) tools have the potential to support individuals managing chronic stroke symptoms. OBJECTIVES This scoping review identifies prevalent topics and concepts in research literature on IT technology for stroke rehabilitation and reintegration, utilizing content analysis, based on topic modelling techniques from natural language processing to identify gaps in this literature. ELIGIBILITY CRITERIA Our methodological search initially identified over 14,000 publications of the last two decades in the Web of Science and Scopus databases, which we filter, using keywords and a qualitative review, to a core corpus of 1062 documents. RESULTS We generate a 3-topic, 4-topic and 5-topic model and interpret the resulting topics as four distinct thematics in the literature, which we label as Robotics, Software, Functional and Cognitive. We analyze the prevalence and distinctiveness of each thematic and identify some areas relatively neglected by the field. These are mainly in the Cognitive thematic, especially for systems and devices for sensory loss rehabilitation, tasks of daily living performance and social participation. CONCLUSION The results indicate that IT-enabled stroke literature has focused on Functional outcomes and Robotic technologies, with lesser emphasis on Cognitive outcomes and combined interventions. We hope this review broadens awareness, usage and mainstream acceptance of novel technologies in rehabilitation and reintegration among clinicians, carers and patients.
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Xu B, Liu D, Xue M, Miao M, Hu C, Song A. Continuous shared control of a mobile robot with brain-computer interface and autonomous navigation for daily assistance. Comput Struct Biotechnol J 2023; 22:3-16. [PMID: 37600142 PMCID: PMC10433001 DOI: 10.1016/j.csbj.2023.07.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 07/04/2023] [Accepted: 07/22/2023] [Indexed: 08/22/2023] Open
Abstract
Although the electroencephalography (EEG) based brain-computer interface (BCI) has been successfully developed for rehabilitation and assistance, it is still challenging to achieve continuous control of a brain-actuated mobile robot system. In this study, we propose a continuous shared control strategy combining continuous BCI and autonomous navigation for a mobile robot system. The weight of shared control is designed to dynamically adjust the fusion of continuous BCI control and autonomous navigation. During this process, the system uses the visual-based simultaneous localization and mapping (SLAM) method to construct environmental maps. After obtaining the global optimal path, the system utilizes the brain-based shared control dynamic window approach (BSC-DWA) to evaluate safe and reachable trajectories while considering shared control velocity. Eight subjects participated in two-stage training, and six of these eight subjects participated in online shared control experiments. The training results demonstrated that naïve subjects could achieve continuous control performance with an average percent valid correct rate of approximately 97 % and an average total correct rate of over 80 %. The results of online shared control experiments showed that all of the subjects could complete navigation tasks in an unknown corridor with continuous shared control. Therefore, our experiments verified the feasibility and effectiveness of the proposed system combining continuous BCI, shared control, autonomous navigation, and visual SLAM. The proposed continuous shared control framework shows great promise in BCI-driven tasks, especially navigation tasks for brain-driven assistive mobile robots and wheelchairs in daily applications.
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Affiliation(s)
- Baoguo Xu
- State Key Laboratory of Bioelectronics, Jiangsu Key Laboratory of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
| | - Deping Liu
- State Key Laboratory of Bioelectronics, Jiangsu Key Laboratory of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
| | - Muhui Xue
- State Key Laboratory of Bioelectronics, Jiangsu Key Laboratory of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
| | - Minmin Miao
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Cong Hu
- Guangxi Key Laboratory of Automatic Detecting Technology and Instruments, Guilin University of Electronic Technology, Guilin 541004, China
| | - Aiguo Song
- State Key Laboratory of Bioelectronics, Jiangsu Key Laboratory of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
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Saichoo T, Boonbrahm P, Punsawad Y. Investigating User Proficiency of Motor Imagery for EEG-Based BCI System to Control Simulated Wheelchair. SENSORS (BASEL, SWITZERLAND) 2022; 22:9788. [PMID: 36560158 PMCID: PMC9781917 DOI: 10.3390/s22249788] [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: 11/16/2022] [Revised: 12/01/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
The research on the electroencephalography (EEG)-based brain-computer interface (BCI) is widely utilized for wheelchair control. The ability of the user is one factor of BCI efficiency. Therefore, we focused on BCI tasks and protocols to yield high efficiency from the robust EEG features of individual users. This study proposes a task-based brain activity to gain the power of the alpha band, which included eyes closed for alpha response at the occipital area, attention to an upward arrow for alpha response at the frontal area, and an imagined left/right motor for alpha event-related desynchronization at the left/right motor cortex. An EPOC X neuroheadset was used to acquire the EEG signals. We also proposed user proficiency in motor imagery sessions with limb movement paradigms by recommending motor imagination tasks. Using the proposed system, we verified the feature extraction algorithms and command translation. Twelve volunteers participated in the experiment, and the conventional paradigm of motor imagery was used to compare the efficiencies. With utilized user proficiency in motor imagery, an average accuracy of 83.7% across the left and right commands was achieved. The recommended MI paradigm via user proficiency achieved an approximately 4% higher accuracy than the conventional MI paradigm. Moreover, the real-time control results of a simulated wheelchair revealed a high efficiency based on the time condition. The time results for the same task as the joystick-based control were still approximately three times longer. We suggest that user proficiency be used to recommend an individual MI paradigm for beginners. Furthermore, the proposed BCI system can be used for electric wheelchair control by people with severe disabilities.
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Affiliation(s)
- Theerat Saichoo
- School of Informatics, Walailak University, Nakhon Si Thammarat 80160, Thailand
| | - Poonpong Boonbrahm
- School of Informatics, Walailak University, Nakhon Si Thammarat 80160, Thailand
| | - Yunyong Punsawad
- School of Informatics, Walailak University, Nakhon Si Thammarat 80160, Thailand
- Informatics Innovative Center of Excellence, Walailak University, Nakhon Si Thammarat 80160, Thailand
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Floreani ED, Rowley D, Kelly D, Kinney-Lang E, Kirton A. On the feasibility of simple brain-computer interface systems for enabling children with severe physical disabilities to explore independent movement. Front Hum Neurosci 2022; 16:1007199. [PMID: 36337857 PMCID: PMC9633669 DOI: 10.3389/fnhum.2022.1007199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 10/03/2022] [Indexed: 12/04/2022] Open
Abstract
Introduction Children with severe physical disabilities are denied their fundamental right to move, restricting their development, independence, and participation in life. Brain-computer interfaces (BCIs) could enable children with complex physical needs to access power mobility (PM) devices, which could help them move safely and independently. BCIs have been studied for PM control for adults but remain unexamined in children. In this study, we explored the feasibility of BCI-enabled PM control for children with severe physical disabilities, assessing BCI performance, standard PM skills and tolerability of BCI. Materials and methods Patient-oriented pilot trial. Eight children with quadriplegic cerebral palsy attended two sessions where they used a simple, commercial-grade BCI system to activate a PM trainer device. Performance was assessed through controlled activation trials (holding the PM device still or activating it upon verbal and visual cueing), and basic PM skills (driving time, number of activations, stopping) were assessed through distance trials. Setup and calibration times, headset tolerability, workload, and patient/caregiver experience were also evaluated. Results All participants completed the study with favorable tolerability and no serious adverse events or technological challenges. Average control accuracy was 78.3 ± 12.1%, participants were more reliably able to activate (95.7 ± 11.3%) the device than hold still (62.1 ± 23.7%). Positive trends were observed between performance and prior BCI experience and age. Participants were able to drive the PM device continuously an average of 1.5 meters for 3.0 s. They were able to stop at a target 53.1 ± 23.3% of the time, with significant variability. Participants tolerated the headset well, experienced mild-to-moderate workload and setup/calibration times were found to be practical. Participants were proud of their performance and both participants and families were eager to participate in future power mobility sessions. Discussion BCI-enabled PM access appears feasible in disabled children based on evaluations of performance, tolerability, workload, and setup/calibration. Performance was comparable to existing pediatric BCI literature and surpasses established cut-off thresholds (70%) of “effective” BCI use. Participants exhibited PM skills that would categorize them as “emerging operational learners.” Continued exploration of BCI-enabled PM for children with severe physical disabilities is justified.
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Affiliation(s)
- Erica D. Floreani
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- *Correspondence: Erica D. Floreani,
| | - Danette Rowley
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Alberta Children’s Hospital, Alberta Health Services, Calgary, AB, Canada
| | - Dion Kelly
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Eli Kinney-Lang
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Adam Kirton
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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Leemhuis E, Favieri F, Forte G, Pazzaglia M. Integrated Neuroregenerative Techniques for Plasticity of the Injured Spinal Cord. Biomedicines 2022; 10:biomedicines10102563. [PMID: 36289825 PMCID: PMC9599452 DOI: 10.3390/biomedicines10102563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/18/2022] [Accepted: 10/10/2022] [Indexed: 11/16/2022] Open
Abstract
On the slow path to improving the life expectancy and quality of life of patients post spinal cord injury (SCI), recovery remains controversial. The potential role of the regenerative capacity of the nervous system has led to numerous attempts to stimulate the SCI to re-establish the interrupted sensorimotor loop and to understand its potential in the recovery process. Numerous resources are now available, from pharmacological to biomolecular approaches and from neuromodulation to sensorimotor rehabilitation interventions based on the use of various neural interfaces, exoskeletons, and virtual reality applications. The integration of existing resources seems to be a promising field of research, especially from the perspective of improving living conditions in the short to medium term. Goals such as reducing chronic forms of neuropathic pain, regaining control over certain physiological activities, and enhancing residual abilities are often more urgent than complete functional recovery. In this perspective article, we provide an overview of the latest interventions for the treatment of SCI through broad phases of injury rehabilitation. The underlying intention of this work is to introduce a spinal cord neuroplasticity-based multimodal approach to promote functional recovery and improve quality of life after SCI. Nonetheless, when used separately, biomolecular therapeutic approaches have been shown to have modest outcomes.
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Affiliation(s)
- Erik Leemhuis
- Dipartimento di Psicologia, Sapienza Università di Roma, 00185 Rome, Italy
- Body and Action Lab, IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
- Correspondence: (E.L.); (M.P.)
| | - Francesca Favieri
- Dipartimento di Psicologia, Sapienza Università di Roma, 00185 Rome, Italy
- Body and Action Lab, IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
| | - Giuseppe Forte
- Body and Action Lab, IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
- Dipartimento di Psicologia Dinamica, Clinica e Salute, Sapienza Università di Roma, 00185 Roma, Italy
| | - Mariella Pazzaglia
- Dipartimento di Psicologia, Sapienza Università di Roma, 00185 Rome, Italy
- Body and Action Lab, IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
- Correspondence: (E.L.); (M.P.)
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12
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Usability Evaluation of the SmartWheeler through Qualitative and Quantitative Studies. SENSORS 2022; 22:s22155627. [PMID: 35957184 PMCID: PMC9371067 DOI: 10.3390/s22155627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 07/12/2022] [Accepted: 07/22/2022] [Indexed: 12/10/2022]
Abstract
Background: Intelligent powered wheelchairs remain a popular research topic that can improve users’ quality of life. Although our multidisciplinary research team has put a lot of effort into adding features based on end-users needs and impairments since 2006, there are still open issues regarding the usability and functionalities of an intelligent powered wheelchair (IPW). Methods: For this reason, this research presents an experience with our IPW followed by a study in two parts: a quantitative one based on the System Usability Scale (SUS) questionnaire and a qualitative one through open questions regarding IPW functionalities with novice users, e.g., IPW non-users. These users never used an IPW before, but are users and aware of the impacts of the technology used in our IPW, being undergraduate to postdoctoral students and staff (faculty, lecturers, research engineers) at the Faculty of Engineering of Université de Sherbrooke. Results: The qualitative analyses identified different behaviours among the novice users. The quantitative analysis via SUS questionnaire done with novice users reports an “okay” rating (equivalent with a C grade or 68 SUS Score) for our IPW’s usability. Moreover, advantages and disadvantages opinions were gathered on the IPW as well as comments which can be used to improve the system. Conclusions: The results reported in these studies show that the system, e.g., IPW, was judged to be sufficiently usable and robust by novice users, with and without experience with the software used in developing the IPW.
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Liu K, Yu Y, Liu Y, Tang J, Liang X, Chu X, Zhou Z. A novel brain-controlled wheelchair combined with computer vision and augmented reality. Biomed Eng Online 2022; 21:50. [PMID: 35883092 PMCID: PMC9327337 DOI: 10.1186/s12938-022-01020-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 07/11/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Brain-controlled wheelchairs (BCWs) are important applications of brain-computer interfaces (BCIs). Currently, most BCWs are semiautomatic. When users want to reach a target of interest in their immediate environment, this semiautomatic interaction strategy is slow. METHODS To this end, we combined computer vision (CV) and augmented reality (AR) with a BCW and proposed the CVAR-BCW: a BCW with a novel automatic interaction strategy. The proposed CVAR-BCW uses a translucent head-mounted display (HMD) as the user interface, uses CV to automatically detect environments, and shows the detected targets through AR technology. Once a user has chosen a target, the CVAR-BCW can automatically navigate to it. For a few scenarios, the semiautomatic strategy might be useful. We integrated a semiautomatic interaction framework into the CVAR-BCW. The user can switch between the automatic and semiautomatic strategies. RESULTS We recruited 20 non-disabled subjects for this study and used the accuracy, information transfer rate (ITR), and average time required for the CVAR-BCW to reach each designated target as performance metrics. The experimental results showed that our CVAR-BCW performed well in indoor environments: the average accuracies across all subjects were 83.6% (automatic) and 84.1% (semiautomatic), the average ITRs were 8.2 bits/min (automatic) and 8.3 bits/min (semiautomatic), the average times required to reach a target were 42.4 s (automatic) and 93.4 s (semiautomatic), and the average workloads and degrees of fatigue for the two strategies were both approximately 20. CONCLUSIONS Our CVAR-BCW provides a user-centric interaction approach and a good framework for integrating more advanced artificial intelligence technologies, which may be useful in the field of disability assistance.
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Affiliation(s)
- Kaixuan Liu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, Hunan, China
| | - Yang Yu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, Hunan, China.
| | - Yadong Liu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, Hunan, China
| | - Jingsheng Tang
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, Hunan, China
| | - Xinbin Liang
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, Hunan, China
| | - Xingxing Chu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, Hunan, China
| | - Zongtan Zhou
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, Hunan, China
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Mumtaz W, Amin HU, Qayyum A, Subhani AR. Editorial: EEG-based assistive robotics for rehabilitation. Front Neurorobot 2022; 16:952495. [PMID: 35909579 PMCID: PMC9332930 DOI: 10.3389/fnbot.2022.952495] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 06/27/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Wajid Mumtaz
- Department of Electrical Engineering, School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad, Pakistan
| | - Hafeez Ullah Amin
- School of Computer Science, University of Nottingham Malaysia, Semenyih, Malaysia
- *Correspondence: Hafeez Ullah Amin
| | - Abdul Qayyum
- Department Vision and Robotics, Université de Bourgogne, Dijon, France
| | - Ahmad Rauf Subhani
- Department of Electrical Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad, Pakistan
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Application of Neuroengineering Based on EEG Features in the Industrial Design of Comfort. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4667689. [PMID: 35720909 PMCID: PMC9205692 DOI: 10.1155/2022/4667689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 04/23/2022] [Accepted: 04/26/2022] [Indexed: 11/17/2022]
Abstract
The smart wheelchair is a service robot that can be used as a means of transportation for the elderly and the disabled. The patients were given an intelligent wheelchair designed by electroencephalogram (EEG), which was used for more than 8 hours and tested continuously for 1 month. By ridit analysis, the difference between the two groups was statistically significant (U = 3.72, P < 0.01). The scores of visual analogue scale (VAS) and joint ground visuality (JGV) in the observation group were significantly better than those in the control group. The modules of physiological function (PF), physical pain (PP), overall health (OH), vitality (VT), social function (SF), emotional function (EF), and mental health (MH) in the SF-36 scores of the two groups were significantly improved (P < 0.05), and the improvement of each module in the observation group was significantly better than that in the control group (P < 0.05). The levels of serum IL-6, IL-10, and superoxide dismutase (SOD) in the two groups were significantly improved (P < 0.05), and the improvement of serum IL-6, IL-10, and SOD in the observation group was significantly better than that in the control group (P < 0.05). It is suggested that neural engineering based on EEG characteristics can be well applied in comfort industrial design.
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Chen X, Yu Y, Tang J, Zhou L, Liu K, Liu Z, Chen S, Wang J, Zeng LL, Liu J, Hu D. Clinical validation of BCI-controlled wheelchairs in subjects with severe spinal cord injury. IEEE Trans Neural Syst Rehabil Eng 2022; 30:579-589. [PMID: 35259107 DOI: 10.1109/tnsre.2022.3156661] [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
Brain-controlled wheelchairs are one of the most promising applications that can help people gain mobility after their normal interaction pathways have been compromised by neuromuscular diseases. The feasibility of using brain signals to control wheelchairs has been well demonstrated by healthy people in previous studies. However, most potential users of brain-controlled wheelchairs are people suffering from severe physical disabilities or who are in a "locked-in" state. To further validate the clinical practicability of our previously proposed P300-based brain-controlled wheelchair, in this study, 10 subjects with severe spinal cord injuries participated in three experiments and completed ten predefined tasks in each experiment. The average accuracy and information transfer rate (ITR) were 94.8% and 4.2 bits/min, respectively. Moreover, we evaluated the physiological and cognitive burdens experienced by these individuals before and after the experiments. There were no significant changes in vital signs during the experiment, indicating minimal physiological and cognitive burden. The patients' average systolic blood pressure before and after the experiment was 113±13.7 mmHg and 114±11.9 mmHg, respectively (P=0.122). The patients' average heart rates before and after the experiment were 79±8.4/min and 79±8.2/min, respectively (P=0.147). The average task load, measured by the National Aeronautics and Space Administration task load index, ranged from 10.0 to 25.5. The results suggest that the proposed P300-based brain-controlled wheelchair is safe and reliable; additionally, it does not significantly increase the patient's physical and mental task burden, demonstrating its potential value in clinical applications. Our study promotes the development of a more practical brain-controlled wheelchair system.
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A Brain-Computer Interface for Teleoperation of a Semiautonomous Mobile Robotic Assistive System Using SLAM. JOURNAL OF ROBOTICS 2022. [DOI: 10.1155/2022/6178917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The proposed assistive hybrid brain-computer interface (BCI) semiautonomous mobile robotic arm demonstrates a design that is (1) adaptable by observing environmental changes with sensors and deploying alternate solutions and (2) versatile by receiving commands from the user’s brainwave signals through a noninvasive electroencephalogram cap. Composed of three integrated subsystems, a hybrid BCI controller, an omnidirectional mobile base, and a robotic arm, the proposed robot has commands mapped to the user’s brainwaves related to a set of specific physical or mental tasks. The implementation of sensors and the camera systems enable both the mobile base and the arm to be semiautonomous. The mobile base’s SLAM algorithm has obstacle avoidance capability and path planning to assist the robot maneuver safely. The robot arm calculates and deploys the necessary joint movement to pick up or drop off a desired object selected by the user via a brainwave controlled cursor on a camera feed. Validation, testing, and implementation of the subsystems were conducted using Gazebo. Communication between the BCI controller and the subsystems is tested independently. A loop of prerecorded brainwave data related to each specific task is used to ensure that the mobile base command is executed; the same prerecorded file is used to move the robot arm cursor and initiate a pick-up or drop-off action. A final system test is conducted where the BCI controller input moves the cursor and selects a goal point. Successful virtual demonstrations of the assistive robotic arm show the feasibility of restoring movement capability and autonomy for a disabled user.
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18
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Traded and combined cooperative control of a smart wheelchair. ROBOTICA 2022. [DOI: 10.1017/s0263574721001855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Abstract
This article deals with a human–machine cooperative system for the control of a smart wheelchair for people with motor disabilities. The choice of a traded control mode is first argued. The paper then pursues two objectives. The first is to describe the design of the cooperative system by focusing on the dialogue and the interaction between the pilot and the robot. The second objective is to introduce a new cooperative mode. In this one, three features are proposed: two semi-autonomous features, a wall following and a doorway crossing, during which the user can intervene punctually to rectify a trajectory or a path, and an assisted mode where, conversely, the machine intervenes in a manual control to avoid obstacles. This mode of intervention of an entity, human or machine, supervising a movement controlled by the other is referred as “combined control.” Examples of scenarios exploiting the cooperative capabilities of the system are presented and discussed.
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Steering a Robotic Wheelchair Based on Voice Recognition System Using Convolutional Neural Networks. ELECTRONICS 2022. [DOI: 10.3390/electronics11010168] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Many wheelchair people depend on others to control the movement of their wheelchairs, which significantly influences their independence and quality of life. Smart wheelchairs offer a degree of self-dependence and freedom to drive their own vehicles. In this work, we designed and implemented a low-cost software and hardware method to steer a robotic wheelchair. Moreover, from our method, we developed our own Android mobile app based on Flutter software. A convolutional neural network (CNN)-based network-in-network (NIN) structure approach integrated with a voice recognition model was also developed and configured to build the mobile app. The technique was also implemented and configured using an offline Wi-Fi network hotspot between software and hardware components. Five voice commands (yes, no, left, right, and stop) guided and controlled the wheelchair through the Raspberry Pi and DC motor drives. The overall system was evaluated based on a trained and validated English speech corpus by Arabic native speakers for isolated words to assess the performance of the Android OS application. The maneuverability performance of indoor and outdoor navigation was also evaluated in terms of accuracy. The results indicated a degree of accuracy of approximately 87.2% of the accurate prediction of some of the five voice commands. Additionally, in the real-time performance test, the root-mean-square deviation (RMSD) values between the planned and actual nodes for indoor/outdoor maneuvering were 1.721 × 10−5 and 1.743 × 10−5, respectively.
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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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21
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Mridha MF, Das SC, Kabir MM, Lima AA, Islam MR, Watanobe Y. Brain-Computer Interface: Advancement and Challenges. SENSORS 2021; 21:s21175746. [PMID: 34502636 PMCID: PMC8433803 DOI: 10.3390/s21175746] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 08/15/2021] [Accepted: 08/20/2021] [Indexed: 02/04/2023]
Abstract
Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the last decades, several groundbreaking research has been conducted in this domain. Still, no comprehensive review that covers the BCI domain completely has been conducted yet. Hence, a comprehensive overview of the BCI domain is presented in this study. This study covers several applications of BCI and upholds the significance of this domain. Then, each element of BCI systems, including techniques, datasets, feature extraction methods, evaluation measurement matrices, existing BCI algorithms, and classifiers, are explained concisely. In addition, a brief overview of the technologies or hardware, mostly sensors used in BCI, is appended. Finally, the paper investigates several unsolved challenges of the BCI and explains them with possible solutions.
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Affiliation(s)
- M. F. Mridha
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh; (M.F.M.); (S.C.D.); (M.M.K.); (A.A.L.)
| | - Sujoy Chandra Das
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh; (M.F.M.); (S.C.D.); (M.M.K.); (A.A.L.)
| | - Muhammad Mohsin Kabir
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh; (M.F.M.); (S.C.D.); (M.M.K.); (A.A.L.)
| | - Aklima Akter Lima
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh; (M.F.M.); (S.C.D.); (M.M.K.); (A.A.L.)
| | - Md. Rashedul Islam
- Department of Computer Science and Engineering, University of Asia Pacific, Dhaka 1216, Bangladesh
- Correspondence:
| | - Yutaka Watanobe
- Department of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu 965-8580, Japan;
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Jamil N, Belkacem AN, Ouhbi S, Lakas A. Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain-Computer Interfaces: A Systematic Literature Review. SENSORS (BASEL, SWITZERLAND) 2021; 21:4754. [PMID: 34300492 PMCID: PMC8309653 DOI: 10.3390/s21144754] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 06/28/2021] [Accepted: 07/09/2021] [Indexed: 11/30/2022]
Abstract
Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain-computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited to studies focusing on applications and devices of the BCI technology. The data used herein were selected using IC and exclusion criteria to ensure quality assessment. The selected articles were divided into four main research areas: education, engineering, entertainment, and medicine. Overall, 238 papers were selected based on IC. Moreover, 28 companies were identified that developed wired and wireless equipment as means of BCI assistive technology. The findings of this review indicate that the implications of using BCIs for assistive, adaptive, and rehabilitative technologies are encouraging for people with severe motor disabilities and healthy people. With an increasing number of healthy people using BCIs, other research areas, such as the motivation of players when participating in games or the security of soldiers when observing certain areas, can be studied and collaborated using the BCI technology. However, such BCI systems must be simple (wearable), convenient (sensor fabrics and self-adjusting abilities), and inexpensive.
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Affiliation(s)
- Nuraini Jamil
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; (N.J.); (S.O.)
| | - Abdelkader Nasreddine Belkacem
- Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates;
| | - Sofia Ouhbi
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates; (N.J.); (S.O.)
| | - Abderrahmane Lakas
- Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates;
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Exploring the Use of Brain-Computer Interfaces in Stroke Neurorehabilitation. BIOMED RESEARCH INTERNATIONAL 2021; 2021:9967348. [PMID: 34239936 PMCID: PMC8235968 DOI: 10.1155/2021/9967348] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 06/04/2021] [Indexed: 11/17/2022]
Abstract
With the continuous development of artificial intelligence technology, "brain-computer interfaces" are gradually entering the field of medical rehabilitation. As a result, brain-computer interfaces (BCIs) have been included in many countries' strategic plans for innovating this field, and subsequently, major funding and talent have been invested in this technology. In neurological rehabilitation for stroke patients, the use of BCIs opens up a new chapter in "top-down" rehabilitation. In our study, we first reviewed the latest BCI technologies, then presented recent research advances and landmark findings in BCI-based neurorehabilitation for stroke patients. Neurorehabilitation was focused on the areas of motor, sensory, speech, cognitive, and environmental interactions. Finally, we summarized the shortcomings of BCI use in the field of stroke neurorehabilitation and the prospects for BCI technology development for rehabilitation.
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Design and Analysis of an Intelligent Toilet Wheelchair Based on Planar 2DOF Parallel Mechanism with Coupling Branch Chains. SENSORS 2021; 21:s21082677. [PMID: 33920238 PMCID: PMC8069320 DOI: 10.3390/s21082677] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 04/08/2021] [Accepted: 04/08/2021] [Indexed: 11/17/2022]
Abstract
Due to the fixed size of the structure or the possibility of only simple manual adjustment, the traditional toilet wheelchair cannot easily be adapted to the size of the user or the toilet. In this paper, a planar two-degree-of-freedom parallel mechanism with coupling branch chains is proposed to enable both seat height adjustment and body posture adjustment of a toilet chair, solving the problems of posture adaptability between the user and the machine, and height matching in the process of using the wheelchair-assisted toilet. The model of the parallel mechanism was designed after analyzing the general rules of posture transformation in the human body before and after the toilet process, and the dimensions of each linkage were then determined according to the constraint conditions. By analyzing the degree of freedom, kinematics, workspace, singularity and position of the center of gravity, the rationality of the design was ensured. The weighted average function was used to find the optimal fixed point of the horizontal moving slider, and the actual trajectory at the end of the single driving mode was close to the ideal trajectory. The experimental results show that the adjustable seat height range is 290~550 mm and the adjustable angle range is 0~90°, which can enable disabled people to use the toilet independently.
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Zuo C, Jin J, Xu R, Wu L, Liu C, Miao Y, Wang X. Cluster decomposing and multi-objective optimization based-ensemble learning framework for motor imagery-based brain-computer interfaces. J Neural Eng 2021; 18. [PMID: 33524961 DOI: 10.1088/1741-2552/abe20f] [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] [Received: 10/10/2020] [Accepted: 02/01/2021] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Motor imagery (MI) is a mental representation of motor behavior and a widely used pattern in electroencephalogram (EEG) based brain-computer interface (BCI) systems. EEG is known for its non-stationary, non-linear features and sensitivity to artifacts from various sources. This study aimed to design a powerful classifier with a strong generalization capability for MI based BCIs. APPROACH In this study, we proposed a cluster decomposing based ensemble learning framework (CDECL) for EEG classification of MI based BCIs. The EEG data was decomposed into sub-data sets with different distributions by clustering decomposition. Then a set of heterogeneous classifiers was trained on each sub-data set for generating a diversified classifier search space. To obtain the optimal classifier combination, the ensemble learning was formulated as a multi-objective optimization problem and a stochastic fractal based binary multi-objective fruit fly optimization algorithm was proposed for solving the ensemble learning problem. MAIN RESULTS The proposed method was validated on two public EEG datasets (BCI Competition IV datasets IIb and BCI Competition IV dataset IIa) and compared with several other competing classification methods. Experimental results showed that the proposed CDECL based methods can effectively construct a diversity ensemble classifier and exhibits superior classification performance in comparison with several competing methods. SIGNIFICANCE The proposed method is promising for improving the performance of MI-based BCIs.
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Affiliation(s)
- Cili Zuo
- East China University of Science and Technology, 130 Meilong road, Shanghai, Shanghai, 200237, CHINA
| | - Jing Jin
- Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education, East China University of Science and Technology, 130 Meilong Road, Xuhui District, Shanghai 200237, SHANGHAI, 200237, CHINA
| | - Ren Xu
- Guger Technologies OG, Sierningstrasse 14, Graz, 8020, AUSTRIA
| | - Lianghong Wu
- Hunan University of Science and Technology, Tiaoyuan Road, Xiangtan, 411201, CHINA
| | - Chang Liu
- East China University of Science and Technology, 130 Meilong Road, Shanghai, Shanghai, 200237, CHINA
| | - Yangyang Miao
- East China University of Science and Technology, 130 Meilong raod, Shanghai, Shanghai, 200237, CHINA
| | - Xingyu Wang
- East China University of Science and Technology, 130 Meilong Road, Shanghai, Shanghai, 200237, CHINA
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Temporal frequency joint sparse optimization and fuzzy fusion for motor imagery-based brain-computer interfaces. J Neurosci Methods 2020; 340:108725. [DOI: 10.1016/j.jneumeth.2020.108725] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 04/02/2020] [Accepted: 04/03/2020] [Indexed: 11/20/2022]
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27
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王 金, 王 康, 陈 小, 王 慧, 徐 圣, 刘 明. [Indoor simulation training system for brain-controlled wheelchair based on steady-state visual evoked potentials]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2020; 37:502-511. [PMID: 32597093 PMCID: PMC10319571 DOI: 10.7507/1001-5515.201906025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Indexed: 11/03/2022]
Abstract
Brain-controlled wheelchair (BCW) is one of the important applications of brain-computer interface (BCI) technology. The present research shows that simulation control training is of great significance for the application of BCW. In order to improve the BCW control ability of users and promote the application of BCW under the condition of safety, this paper builds an indoor simulation training system based on the steady-state visual evoked potentials for BCW. The system includes visual stimulus paradigm design and implementation, electroencephalogram acquisition and processing, indoor simulation environment modeling, path planning, and simulation wheelchair control, etc. To test the performance of the system, a training experiment involving three kinds of indoor path-control tasks is designed and 10 subjects were recruited for the 5-day training experiment. By comparing the results before and after the training experiment, it was found that the average number of commands in Task 1, Task 2, and Task 3 decreased by 29.5%, 21.4%, and 25.4%, respectively ( P < 0.001). And the average number of commands used by the subjects to complete all tasks decreased by 25.4% ( P < 0.001). The experimental results show that the training of subjects through the indoor simulation training system built in this paper can improve their proficiency and efficiency of BCW control to a certain extent, which verifies the practicability of the system and provides an effective assistant method to promote the indoor application of BCW.
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Affiliation(s)
- 金海 王
- 天津工业大学 电子与信息工程学院(天津 300387)School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, P.R.China
| | - 康宁 王
- 天津工业大学 电子与信息工程学院(天津 300387)School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, P.R.China
- 中国医学科学院 北京协和医学院 生物医学工程研究所(天津 300192)Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300192, P.R.China
| | - 小刚 陈
- 天津工业大学 电子与信息工程学院(天津 300387)School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, P.R.China
| | - 慧泉 王
- 天津工业大学 电子与信息工程学院(天津 300387)School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, P.R.China
| | - 圣普 徐
- 天津工业大学 电子与信息工程学院(天津 300387)School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, P.R.China
| | - 明 刘
- 天津工业大学 电子与信息工程学院(天津 300387)School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, P.R.China
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Rashid M, Sulaiman N, P P Abdul Majeed A, Musa RM, Ab Nasir AF, Bari BS, Khatun S. Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review. Front Neurorobot 2020; 14:25. [PMID: 32581758 PMCID: PMC7283463 DOI: 10.3389/fnbot.2020.00025] [Citation(s) in RCA: 102] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 04/08/2020] [Indexed: 12/12/2022] Open
Abstract
Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.
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Affiliation(s)
- Mamunur Rashid
- Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia
| | - Norizam Sulaiman
- Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia
| | - Anwar P P Abdul Majeed
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia
| | - Rabiu Muazu Musa
- Centre for Fundamental and Continuing Education, Universiti Malaysia Terengganu, Kuala Nerus, Malaysia
| | - Ahmad Fakhri Ab Nasir
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia
| | - Bifta Sama Bari
- Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia
| | - Sabira Khatun
- Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia
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Liu M, Wang K, Chen X, Zhao J, Chen Y, Wang H, Wang J, Xu S. Indoor Simulated Training Environment for Brain-Controlled Wheelchair Based on Steady-State Visual Evoked Potentials. Front Neurorobot 2020; 13:101. [PMID: 31998108 PMCID: PMC6961652 DOI: 10.3389/fnbot.2019.00101] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 11/19/2019] [Indexed: 12/29/2022] Open
Abstract
Brain-controlled wheelchair (BCW) has the potential to improve the quality of life for people with motor disabilities. A lot of training is necessary for users to learn and improve BCW control ability and the performances of BCW control are crucial for patients in daily use. In consideration of safety and efficiency, an indoor simulated training environment is built up in this paper to improve the performance of BCW control. The indoor simulated environment mainly realizes BCW implementation, simulated training scenario setup, path planning and recommendation, simulated operation, and scoring. And the BCW is based on steady-state visual evoked potentials (SSVEP) and the filter bank canonical correlation analysis (FBCCA) is used to analyze the electroencephalography (EEG). Five tasks include individual accuracy, simple linear path, obstacles avoidance, comprehensive steering scenarios, and evaluation task are designed, 10 healthy subjects were recruited and carried out the 7-days training experiment to assess the performance of the training environment. Scoring and command-consuming are conducted to evaluate the improvement before and after training. The results indicate that the average accuracy is 93.55% and improves from 91.05% in the first stage to 96.05% in the second stage (p = 0.001). Meanwhile, the average score increases from 79.88 in the first session to 96.66 in the last session and tend to be stable (p < 0.001). The average number of commands and collisions to complete the tasks decreases significantly with or without the approximate shortest path (p < 0.001). These results show that the performance of subjects in BCW control achieves improvement and verify the feasibility and effectiveness of the proposed simulated training environment.
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Affiliation(s)
- Ming Liu
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Kangning Wang
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China.,School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin, China
| | - Xiaogang Chen
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Jing Zhao
- Institute of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Yuanyuan Chen
- School of Microelectronics, Tianjin University, Tianjin, China
| | - Huiquan Wang
- School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin, China
| | - Jinhai Wang
- School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin, China
| | - Shengpu Xu
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
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Zuo C, Jin J, Yin E, Saab R, Miao Y, Wang X, Hu D, Cichocki A. Novel hybrid brain-computer interface system based on motor imagery and P300. Cogn Neurodyn 2019; 14:253-265. [PMID: 32226566 DOI: 10.1007/s11571-019-09560-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 09/19/2019] [Accepted: 10/08/2019] [Indexed: 01/08/2023] Open
Abstract
Motor imagery (MI) is a mental representation of motor behavior and has been widely used in electroencephalogram based brain-computer interfaces (BCIs). Several studies have demonstrated the efficacy of MI-based BCI-feedback training in post-stroke rehabilitation. However, in the earliest stage of the training, calibration data typically contain insufficient discriminability, resulting in unreliable feedback, which may decrease subjects' motivation and even hinder their training. To improve the performance in the early stages of MI training, a novel hybrid BCI paradigm based on MI and P300 is proposed in this study. In this paradigm, subjects are instructed to imagine writing the Chinese character following the flash order of the desired Chinese character displayed on the screen. The event-related desynchronization/synchronization (ERD/ERS) phenomenon is produced with writing based on one's imagination. Simultaneously, the P300 potential is evoked by the flash of each stroke. Moreover, a fusion method of P300 and MI classification is proposed, in which unreliable P300 classifications are corrected by reliable MI classifications. Twelve healthy naïve MI subjects participated in this study. Results demonstrated that the proposed hybrid BCI paradigm yielded significantly better performance than the single-modality BCI paradigm. The recognition accuracy of the fusion method is significantly higher than that of P300 (p < 0.05) and MI (p < 0.01). Moreover, the training data size can be reduced through fusion of these two modalities.
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Affiliation(s)
- Cili Zuo
- 1Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Jing Jin
- 1Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Erwei Yin
- Unmanned Systems Research Center, National Institute of Defense Technology Innovation, Academy of Military Sciences China, Beijing, 100081 People's Republic of China.,Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin, People's Republic of China
| | - Rami Saab
- 4Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada
| | - Yangyang Miao
- 1Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Xingyu Wang
- 1Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Dewen Hu
- 5College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha, 410073 Hunan People's Republic of China
| | - Andrzej Cichocki
- 6Skolkovo Institute of Science and Technology (SKOLTECH), Moscow, Russia 143026.,7Systems Research Institute PAS, Warsaw, Poland.,8Nicolaus Copernicus University (UMK), Torun, Poland
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Xu G, Lin F, Gong M, Li M, Yu H. [A TrAdaBoost-based method for detecting multiple subjects' P300 potentials]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2019; 36:531-540. [PMID: 31441252 PMCID: PMC10319515 DOI: 10.7507/1001-5515.201811025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Indexed: 11/03/2022]
Abstract
Individual differences of P300 potentials lead to that a large amount of training data must be collected to construct pattern recognition models in P300-based brain-computer interface system, which may cause subjects' fatigue and degrade the system performance. TrAdaBoost is a method that transfers the knowledge from source area to target area, which improves learning effect in the target area. Our research purposed a TrAdaBoost-based linear discriminant analysis and a TrAdaBoost-based support vector machine to recognize the P300 potentials across multiple subjects. This method first trains two kinds of classifiers separately by using the data deriving from a small amount of data from same subject and a large amount of data from different subjects. Then it combines all the classifiers with different weights. Compared with traditional training methods that use only a small amount of data from same subject or mixed different subjects' data to directly train, our algorithm improved the accuracies by 19.56% and 22.25% respectively, and improved the information transfer rate of 14.69 bits/min and 15.76 bits/min respectively. The results indicate that the TrAdaBoost-based method has the potential to enhance the generalization ability of brain-computer interface on the individual differences.
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Affiliation(s)
- Guizhi Xu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin 300132, P.R.China;Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, School of Electrical Engineering, Hebei University of Technology, Tianjin 300132, P.R.China
| | - Fang Lin
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin 300132, P.R.China;Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, School of Electrical Engineering, Hebei University of Technology, Tianjin 300132, P.R.China
| | - Minghong Gong
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin 300132, P.R.China;Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, School of Electrical Engineering, Hebei University of Technology, Tianjin 300132, P.R.China
| | - Mengfan Li
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin 300132, P.R.China;Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, School of Electrical Engineering, Hebei University of Technology, Tianjin 300132,
| | - Hongli Yu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin 300132, P.R.China;Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, School of Electrical Engineering, Hebei University of Technology, Tianjin 300132, P.R.China
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