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Li X, Yang S, Fei N, Wang J, Huang W, Hu Y. A Convolutional Neural Network for SSVEP Identification by Using a Few-Channel EEG. Bioengineering (Basel) 2024; 11:613. [PMID: 38927850 PMCID: PMC11200714 DOI: 10.3390/bioengineering11060613] [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: 04/03/2024] [Revised: 05/11/2024] [Accepted: 06/08/2024] [Indexed: 06/28/2024] Open
Abstract
The application of wearable electroencephalogram (EEG) devices is growing in brain-computer interfaces (BCI) owing to their good wearability and portability. Compared with conventional devices, wearable devices typically support fewer EEG channels. Devices with few-channel EEGs have been proven to be available for steady-state visual evoked potential (SSVEP)-based BCI. However, fewer-channel EEGs can cause the BCI performance to decrease. To address this issue, an attention-based complex spectrum-convolutional neural network (atten-CCNN) is proposed in this study, which combines a CNN with a squeeze-and-excitation block and uses the spectrum of the EEG signal as the input. The proposed model was assessed on a wearable 40-class dataset and a public 12-class dataset under subject-independent and subject-dependent conditions. The results show that whether using a three-channel EEG or single-channel EEG for SSVEP identification, atten-CCNN outperformed the baseline models, indicating that the new model can effectively enhance the performance of SSVEP-BCI with few-channel EEGs. Therefore, this SSVEP identification algorithm based on a few-channel EEG is particularly suitable for use with wearable EEG devices.
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Affiliation(s)
- Xiaodong Li
- Orthopedics Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen 518053, China
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong SAR, China
| | - Shuoheng Yang
- Orthopedics Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen 518053, China
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong SAR, China
| | - Ningbo Fei
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong SAR, China
| | - Junlin Wang
- Orthopedics Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen 518053, China
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong SAR, China
| | - Wei Huang
- Department of Rehabilitation, The Second Affiliated Hospital of Guangdong Medical University, Zhanjiang 524003, China
| | - Yong Hu
- Orthopedics Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen 518053, China
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong SAR, China
- Department of Rehabilitation, The Second Affiliated Hospital of Guangdong Medical University, Zhanjiang 524003, China
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2
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Falcon-Caro A, Shirani S, Ferreira JF, Bird JJ, Sanei S. Formulation of Common Spatial Patterns for Multi-Task Hyperscanning BCI. IEEE Trans Biomed Eng 2024; 71:1950-1957. [PMID: 38252565 DOI: 10.1109/tbme.2024.3356665] [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: 01/24/2024]
Abstract
This work proposes a new formulation for common spatial patterns (CSP), often used as a powerful feature extraction technique in brain-computer interfacing (BCI) and other neurological studies. In this approach, applied to multiple subjects' data and named as hyperCSP, the individual covariance and mutual correlation matrices between multiple simultaneously recorded subjects' electroencephalograms are exploited in the CSP formulation. This method aims at effectively isolating the common motor task between multiple heads and alleviate the effects of other spurious or undesired tasks inherently or intentionally performed by the subjects. This technique can provide a satisfactory classification performance while using small data size and low computational complexity. By using the proposed hyperCSP followed by support vector machines classifier, we obtained a classification accuracy of 81.82% over 8 trials in the presence of strong undesired tasks. We hope that this method could reduce the training error in multi-task BCI scenarios. The recorded valuable motor-related hyperscanning dataset is available for public use to promote the research in this area.
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Hu J. Augmented-reality based brain-computer interface of robot control. Heliyon 2024; 10:e26255. [PMID: 38449664 PMCID: PMC10915352 DOI: 10.1016/j.heliyon.2024.e26255] [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: 09/04/2023] [Revised: 02/02/2024] [Accepted: 02/08/2024] [Indexed: 03/08/2024] Open
Abstract
Brain Computer Interface (BCI) is a new approach to human-computer interaction. It can control the external devices directly with the brain without words and body movements. Brain-controlled robot is a major research area in the field of BCI, which organically integrates BCI with robotic systems to achieve safe and effective real-time control of robots using the user's electroencephalogram (EEG). Currently, there are two types of control methods for brain-controlled robots. One is direct control and the other is shared control. Direct brain control has its shortcomings, namely, low control efficiency and easy user fatigue. Shared control technique can effectively improve the control of brain-controlled robots and reduce the thinking ability of brain-controlled robots, thus making it the main control method of brain-controlled robots. The brain-computer collaborative control system based on augmented reality (AR) technology studied in this paper is a human-computer shared control method. In the experimental analysis of virtual reality (VR) systems and AR systems, this paper processes polylines through a series of control vertices with specific coordinates, using the relative distance measured between each point and the starting point as the relative coordinates, and calculates the operational errors of the two types of systems. In the system error of machining broken lines, when the relative coordinates are (10, 20), (40, 50), and (70, 80), the error values of the VR system are 0.17 mm, 0.36 mm, and 0.55 mm, respectively, while the error values of the AR system are 0.11 mm, 0.24 mm, and 0.41 mm, respectively. Therefore, the studies have illustrated the importance of AR systems for the study of brain-computer collaborative control of robots.
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Affiliation(s)
- Junying Hu
- School of Electronic Information Engineering, Ningbo Polytechnic, Ningbo 315000, Zhejiang, China
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4
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Fasipe G, Goršič M, Rahman MH, Rammer J. Community mobility and participation assessment of manual wheelchair users: a review of current techniques and challenges. Front Hum Neurosci 2024; 17:1331395. [PMID: 38249574 PMCID: PMC10796510 DOI: 10.3389/fnhum.2023.1331395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 12/11/2023] [Indexed: 01/23/2024] Open
Abstract
According to the World Health Organization, hundreds of individuals commence wheelchair use daily, often due to an injury such as spinal cord injury or through a condition such as a stroke. However, manual wheelchair users typically experience reductions in individual community mobility and participation. In this review, articles from 2017 to 2023 were reviewed to identify means of measuring community mobility and participation of manual wheelchair users, factors that can impact these aspects, and current rehabilitation techniques for improving them. The selected articles document current best practices utilizing self-surveys, in-clinic assessments, and remote tracking through GPS and accelerometer data, which rehabilitation specialists can apply to track their patients' community mobility and participation accurately. Furthermore, rehabilitation methods such as wheelchair training programs, brain-computer interface triggered functional electric stimulation therapy, and community-based rehabilitation programs show potential to improve the community mobility and participation of manual wheelchair users. Recommendations were made to highlight potential avenues for future research.
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Affiliation(s)
- Grace Fasipe
- Department of Biomedical Engineering, College of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
| | - Maja Goršič
- Department of Biomedical Engineering, College of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
| | - Mohammad Habibur Rahman
- Department of Biomedical Engineering, College of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
- Department of Mechanical Engineering, College of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
| | - Jacob Rammer
- Department of Biomedical Engineering, College of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
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5
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Zhang J, Li J, Huang Z, Huang D, Yu H, Li Z. Recent Progress in Wearable Brain-Computer Interface (BCI) Devices Based on Electroencephalogram (EEG) for Medical Applications: A Review. HEALTH DATA SCIENCE 2023; 3:0096. [PMID: 38487198 PMCID: PMC10880169 DOI: 10.34133/hds.0096] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 10/19/2023] [Indexed: 03/17/2024]
Abstract
Importance: Brain-computer interface (BCI) decodes and converts brain signals into machine instructions to interoperate with the external world. However, limited by the implantation risks of invasive BCIs and the operational complexity of conventional noninvasive BCIs, applications of BCIs are mainly used in laboratory or clinical environments, which are not conducive to the daily use of BCI devices. With the increasing demand for intelligent medical care, the development of wearable BCI systems is necessary. Highlights: Based on the scalp-electroencephalogram (EEG), forehead-EEG, and ear-EEG, the state-of-the-art wearable BCI devices for disease management and patient assistance are reviewed. This paper focuses on the EEG acquisition equipment of the novel wearable BCI devices and summarizes the development direction of wearable EEG-based BCI devices. Conclusions: BCI devices play an essential role in the medical field. This review briefly summarizes novel wearable EEG-based BCIs applied in the medical field and the latest progress in related technologies, emphasizing its potential to help doctors, patients, and caregivers better understand and utilize BCI devices.
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Affiliation(s)
- Jiayan Zhang
- Beijing Advanced Innovation Center for Integrated Circuits, Beijing, China
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, School of Integrated Circuits,
Peking University, Beijing, China
| | - Junshi Li
- Beijing Advanced Innovation Center for Integrated Circuits, Beijing, China
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, School of Integrated Circuits,
Peking University, Beijing, China
| | - Zhe Huang
- Beijing Advanced Innovation Center for Integrated Circuits, Beijing, China
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, School of Integrated Circuits,
Peking University, Beijing, China
- Shenzhen Graduate School,
Peking University, Shenzhen, China
| | - Dong Huang
- Beijing Advanced Innovation Center for Integrated Circuits, Beijing, China
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, School of Integrated Circuits,
Peking University, Beijing, China
- School of Electronics,
Peking University, Beijing, China
| | - Huaiqiang Yu
- Sichuan Institute of Piezoelectric and Acousto-optic Technology, Chongqing, China
| | - Zhihong Li
- Beijing Advanced Innovation Center for Integrated Circuits, Beijing, China
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, School of Integrated Circuits,
Peking University, Beijing, China
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Liu T, Ye A. Domain knowledge-assisted multi-objective evolutionary algorithm for channel selection in brain-computer interface systems. Front Neurosci 2023; 17:1251968. [PMID: 37746153 PMCID: PMC10512944 DOI: 10.3389/fnins.2023.1251968] [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: 07/03/2023] [Accepted: 08/21/2023] [Indexed: 09/26/2023] Open
Abstract
Background For non-invasive brain-computer interface systems (BCIs) with multiple electroencephalogram (EEG) channels, the key factor limiting their convenient application in the real world is how to perform reasonable channel selection while ensuring task accuracy, which can be modeled as a multi-objective optimization problem. Therefore, this paper proposed a two-objective problem model for the channel selection problem and introduced a domain knowledge-assisted multi-objective optimization algorithm (DK-MOEA) to solve the aforementioned problem. Methods The multi-objective optimization problem model was designed based on the channel connectivity matrix and comprises two objectives: one is the task accuracy and the other one can sensitively indicate the removal status of channels in BCIs. The proposed DK-MOEA adopted a two-space framework, consisting of the population space and the knowledge space. Furthermore, a knowledge-assisted update operator was introduced to enhance the search efficiency of the population space by leveraging the domain knowledge stored in the knowledge space. Results The proposed two-objective problem model and DK-MOEA were tested on a fatigue detection task and four state-of-the-art multi-objective evolutionary algorithms were used for comparison. The experimental results indicated that the proposed algorithm achieved the best results among all the comparative algorithms for most cases by the Wilcoxon rank sum test at a significance level of 0.05. DK-MOEA was also compared with a version without the utilization of domain knowledge and the experimental results validated the effectiveness of the knowledge-assisted mutation operator. Moreover, the comparison between DK-MOEA and a traditional classification algorithm using all channels demonstrated that DK-MOEA can strike the balance between task accuracy and the number of selected channels. Conclusion The formulated two-objective optimization model enabled the selection of a minimal number of channels without compromising classification accuracy. The utilization of domain knowledge improved the performance of DK-MOEA. By adopting the proposed two-objective problem model and DK-MOEA, a balance can be achieved between the number of the selected channels and the accuracy of the fatigue detection task. The methods proposed in this paper can reduce the complexity of subsequent data processing and enhance the convenience of practical applications.
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Affiliation(s)
- Tianyu Liu
- School of Information Engineering, Shanghai Maritime University, Shanghai, China
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Huang H, Ramon-Cueto A, El Masri W, Moviglia GA, Saberi H, Sharma HS, Otom A, Chen L, Siniscalco D, Sarnowska A. Advances in Neurorestoratology-Current status and future developments. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2023; 171:207-239. [PMID: 37783556 DOI: 10.1016/bs.irn.2023.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
Neurorestoratology constitutes a novel discipline aimed at the restoration of damaged neural structures and impaired neurological functions. This area of knowledge integrates and compiles all concepts and strategies dealing with the neurorestoration. Although currently, this discipline has already been well recognized by physicians and scientists throughout the world, this article aimed at broadening its knowledge to the academic circle and the public society. Here we shortly introduced why and how Neurorestoratology was born since the fact that the central nervous system (CNS) can be repaired and the subsequent scientific evidence of the neurorestorative mechanisms behind, such as neurostimulation or neuromodulation, neuroprotection, neuroplasticity, neurogenesis, neuroregeneration or axonal regeneration or sprouting, neuroreplacement, loop reconstruction, remyelination, immunoregulation, angiogenesis or revascularization, and others. The scope of this discipline is the improvement of therapeutic approaches for neurological diseases and the development of neurorestorative strategies through the comprehensive efforts of experts in the different areas and all articulated by the associations of Neurorestoratology and its journals. Strikingly, this article additionally explores the "state of art" of the Neurorestoratology field. This includes the development process of the discipline, the achievements and advances of novel neurorestorative treatments, the most efficient procedures exploring and evaluating outcome after the application of pioneer therapies, all the joining of a multidisciplinary expert associations and the specialized journals being more and more impact. We believe that in a near future, this discipline will evolve fast, leading to a general application of cell-based comprehensive neurorestorative treatments to fulfill functional recovery demands for patients with neurological deficits or dysfunctions.
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Affiliation(s)
- Hongyun Huang
- Beijing Hongtianji Neuroscience Academy, Beijing, P.R. China.
| | - Almudena Ramon-Cueto
- Health Center Colmenar Norte, Plaza de Los Ríos 1, Colmenar Viejo, Madrid, Spain
| | - Wagih El Masri
- Robert Jones & Agnes Hunt Orthopaedic Hospital, Spinal Injuries Keele University, Oswestry, United Kingdom
| | - Gustavo A Moviglia
- Wake Forest Institute for Regenerative Medicine. Winston Salem, NC, United States
| | - Hooshang Saberi
- Department of Neurosurgery, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Hari Shanker Sharma
- International Experimental Central Nervous System Injury & Repair (IECNSIR), Dept. of Surgical Sciences, Anesthesiology & Intensive Care Medicine, Uppsala University Hospital, Uppsala University, Uppsala, Sweden
| | - Ali Otom
- Royal Specialty Center for Spine & M-Skeletal Disorders, Amman, Jordan
| | - Lin Chen
- Department of Neurosurgery, Dongzhimen Hospital of Beijing University of Traditional Chinese Medicine, Beijing, P.R. China
| | - Dario Siniscalco
- Department of Experimental Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Anna Sarnowska
- Mossakowski Medical Research Center, Polish Academy of Sciences, Warsaw, Poland
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8
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Wang P, Cao X, Zhou Y, Gong P, Yousefnezhad M, Shao W, Zhang D. A comprehensive review on motion trajectory reconstruction for EEG-based brain-computer interface. Front Neurosci 2023; 17:1086472. [PMID: 37332859 PMCID: PMC10272365 DOI: 10.3389/fnins.2023.1086472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 05/03/2023] [Indexed: 06/20/2023] Open
Abstract
The advance in neuroscience and computer technology over the past decades have made brain-computer interface (BCI) a most promising area of neurorehabilitation and neurophysiology research. Limb motion decoding has gradually become a hot topic in the field of BCI. Decoding neural activity related to limb movement trajectory is considered to be of great help to the development of assistive and rehabilitation strategies for motor-impaired users. Although a variety of decoding methods have been proposed for limb trajectory reconstruction, there does not yet exist a review that covers the performance evaluation of these decoding methods. To alleviate this vacancy, in this paper, we evaluate EEG-based limb trajectory decoding methods regarding their advantages and disadvantages from a variety of perspectives. Specifically, we first introduce the differences in motor execution and motor imagery in limb trajectory reconstruction with different spaces (2D and 3D). Then, we discuss the limb motion trajectory reconstruction methods including experiment paradigm, EEG pre-processing, feature extraction and selection, decoding methods, and result evaluation. Finally, we expound on the open problem and future outlooks.
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Affiliation(s)
| | | | | | | | | | - Wei Shao
- *Correspondence: Wei Shao, ; Daoqiang Zhang,
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9
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Yadav H, Maini S. Electroencephalogram based brain-computer interface: Applications, challenges, and opportunities. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-45. [PMID: 37362726 PMCID: PMC10157593 DOI: 10.1007/s11042-023-15653-x] [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: 10/25/2021] [Revised: 07/17/2022] [Accepted: 04/22/2023] [Indexed: 06/28/2023]
Abstract
Brain-Computer Interfaces (BCI) is an exciting and emerging research area for researchers and scientists. It is a suitable combination of software and hardware to operate any device mentally. This review emphasizes the significant stages in the BCI domain, current problems, and state-of-the-art findings. This article also covers how current results can contribute to new knowledge about BCI, an overview of BCI from its early developments to recent advancements, BCI applications, challenges, and future directions. The authors pointed to unresolved issues and expressed how BCI is valuable for analyzing the human brain. Humans' dependence on machines has led humankind into a new future where BCI can play an essential role in improving this modern world.
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Affiliation(s)
- Hitesh Yadav
- Department of Electrical and Instrumentation Engineering, Sant Longowal Institute of Engineering & Technology, Longowal, Punjab India
| | - Surita Maini
- Department of Electrical and Instrumentation Engineering, Sant Longowal Institute of Engineering & Technology, Longowal, Punjab India
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10
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Bichromatic visual stimulus with subharmonic response to achieve a high-accuracy SSVEP BCI system with low eye irritation. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
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11
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Wang F, Wen Y, Bi J, Li H, Sun J. A portable SSVEP-BCI system for rehabilitation exoskeleton in augmented reality environment. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
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12
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Delisle-Rodriguez D, Silva L, Bastos-Filho T. EEG changes during passive movements improve the motor imagery feature extraction in BCIs-based sensory feedback calibration. J Neural Eng 2023; 20. [PMID: 36716494 DOI: 10.1088/1741-2552/acb73b] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 01/30/2023] [Indexed: 01/31/2023]
Abstract
Objective.This work proposes a method for two calibration schemes based on sensory feedback to extract reliable motor imagery (MI) features, and provide classification outputs more correlated to the user's intention.Method.After filtering the raw electroencephalogram (EEG), a two-step method for spatial feature extraction by using the Riemannian covariance matrices (RCM) method and common spatial patterns is proposed here. It uses EEG data from trials providing feedback, in an intermediate step composed of bothkth nearest neighbors and probability analyses, to find periods of time in which the user probably performed well the MI task without feedback. These periods are then used to extract features with better separability, and train a classifier for MI recognition. For evaluation, an in-house dataset with eight healthy volunteers and two post-stroke patients that performed lower-limb MI, and consequently received passive movements as feedback was used. Other popular public EEG datasets (such as BCI Competition IV dataset IIb, among others) from healthy subjects that executed upper-and lower-limbs MI tasks under continuous visual sensory feedback were further used.Results.The proposed system based on the Riemannian geometry method in two-steps (RCM-RCM) outperformed significantly baseline methods, reaching average accuracy up to 82.29%. These findings show that EEG data on periods providing passive movement can be used to contribute greatly during MI feature extraction.Significance.Unconscious brain responses elicited over the sensorimotor areas may be avoided or greatly reduced by applying our approach in MI-based brain-computer interfaces (BCIs). Therefore, BCI's outputs more correlated to the user's intention can be obtained.
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Affiliation(s)
- Denis Delisle-Rodriguez
- Edmond and Lily Safra International Institute of Neurosciences, Santos Dumont Institute, 59288-899 Macaiba, Brazil
| | - Leticia Silva
- Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo, 29075-910 Vitoria, Brazil
| | - Teodiano Bastos-Filho
- Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo, 29075-910 Vitoria, Brazil
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Alharbi H. Identifying Thematics in a Brain-Computer Interface Research. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:2793211. [PMID: 36643889 PMCID: PMC9833923 DOI: 10.1155/2023/2793211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/21/2022] [Accepted: 12/24/2022] [Indexed: 01/05/2023]
Abstract
This umbrella review is motivated to understand the shift in research themes on brain-computer interfacing (BCI) and it determined that a shift away from themes that focus on medical advancement and system development to applications that included education, marketing, gaming, safety, and security has occurred. The background of this review examined aspects of BCI categorisation, neuroimaging methods, brain control signal classification, applications, and ethics. The specific area of BCI software and hardware development was not examined. A search using One Search was undertaken and 92 BCI reviews were selected for inclusion. Publication demographics indicate the average number of authors on review papers considered was 4.2 ± 1.8. The results also indicate a rapid increase in the number of BCI reviews from 2003, with only three reviews before that period, two in 1972, and one in 1996. While BCI authors were predominantly Euro-American in early reviews, this shifted to a more global authorship, which China dominated by 2020-2022. The review revealed six disciplines associated with BCI systems: life sciences and biomedicine (n = 42), neurosciences and neurology (n = 35), and rehabilitation (n = 20); (2) the second domain centred on the theme of functionality: computer science (n = 20), engineering (n = 28) and technology (n = 38). There was a thematic shift from understanding brain function and modes of interfacing BCI systems to more applied research novel areas of research-identified surround artificial intelligence, including machine learning, pre-processing, and deep learning. As BCI systems become more invasive in the lives of "normal" individuals, it is expected that there will be a refocus and thematic shift towards increased research into ethical issues and the need for legal oversight in BCI application.
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Affiliation(s)
- Hadeel Alharbi
- Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha'il, Ha'il 81481, Saudi Arabia
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14
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EEG Signals to Digit Classification Using Deep Learning-Based One-Dimensional Convolutional Neural Network. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022. [DOI: 10.1007/s13369-022-07313-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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15
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Ye X, Yang C, Chen Y, Wang Y, Gao X, Zhang H. Multisymbol Time Division Coding for High-Frequency Steady-State Visual Evoked Potential-Based Brain-Computer Interface. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1693-1704. [PMID: 35714087 DOI: 10.1109/tnsre.2022.3183087] [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: 11/05/2022]
Abstract
The optimization of coding stimulus is a crucial factor in the study of steady-state visual evoked potential (SSVEP)-based brain-computer interface(BCI).This study proposed an encoding approach named Multi-Symbol Time Division Coding (MSTDC). This approach is based on a protocol of maximizing the distance between neural responses, which aims to encode stimulation systems implementing any number of targets with finite stimulations of different frequencies and phases. Firstly, this study designed an SSVEP-based BCI system containing forty targets with this approach. The stimulation encoding of this system was achieved with four temporal-divided stimuli that adopt the same frequency of 30 Hz and different phases. During the online experiments of twelve subjects, this system achieved an average accuracy of 96.77 ±2.47 % and an average information transfer rate (ITR) of 119.05 ± 6.11 bits/min. This study also devised an SSVEP-based BCI system containing 72 targets and proposed a Template Splicing task-related component analysis (TRCA) algorithm that utilized the dataset of the previous system containing forty targets as the training dataset. The subjects acquired an average accuracy of 86.23 ± 7.75% and an average ITR of 95.68 ± 14.19 bits/min. It can be inferred that MSTDC can encode multiple targets with limited frequencies and phases of stimuli. Meanwhile, this protocol can be effortlessly expanded into other systems and sufficiently reduce the cost of collecting training data. This study provides a feasible technique for obtaining a comfortable SSVEP-based BCI with multiple targets while maintaining high information transfer rate.
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Guo N, Wang X, Duanmu D, Huang X, Li X, Fan Y, Li H, Liu Y, Yeung EHK, To MKT, Gu J, Wan F, Hu Y. SSVEP-Based Brain Computer Interface Controlled Soft Robotic Glove for Post-Stroke Hand Function Rehabilitation. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1737-1744. [PMID: 35731756 DOI: 10.1109/tnsre.2022.3185262] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Soft robotic glove with brain computer interfaces (BCI) control has been used for post-stroke hand function rehabilitation. Motor imagery (MI) based BCI with robotic aided devices has been demonstrated as an effective neural rehabilitation tool to improve post-stroke hand function. It is necessary for a user of MI-BCI to receive a long time training, while the user usually suffers unsuccessful and unsatisfying results in the beginning. To propose another non-invasive BCI paradigm rather than MI-BCI, steady-state visually evoked potentials (SSVEP) based BCI was proposed as user intension detection to trigger the soft robotic glove for post-stroke hand function rehabilitation. Thirty post-stroke patients with impaired hand function were randomly and equally divided into three groups to receive conventional, robotic, and BCI-robotic therapy in this randomized control trial (RCT). Clinical assessment of Fugl-Meyer Motor Assessment of Upper Limb (FMA-UL), Wolf Motor Function Test (WMFT) and Modified Ashworth Scale (MAS) were performed at pre-training, post-training and three months follow-up. In comparing to other groups, The BCI-robotic group showed significant improvement after training in FMA full score (10.05±8.03, p=0.001), FMA shoulder/elbow (6.2±5.94, p=0.0004) and FMA wrist/hand (4.3±2.83, p=0.007), and WMFT (5.1±5.53, p=0.037). The improvement of FMA was significantly correlated with BCI accuracy (r=0.714, p=0.032). Recovery of hand function after rehabilitation of SSVEP-BCI controlled soft robotic glove showed better result than solely robotic glove rehabilitation, equivalent efficacy as results from previous reported MI-BCI robotic hand rehabilitation. It proved the feasibility of SSVEP-BCI controlled soft robotic glove in post-stroke hand function rehabilitation.
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Liu C, Jin J, Daly I, Sun H, Huang Y, Wang X, Cichocki A. Bispectrum-based Hybrid Neural Network for Motor Imagery Classification. J Neurosci Methods 2022; 375:109593. [DOI: 10.1016/j.jneumeth.2022.109593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 03/27/2022] [Accepted: 03/29/2022] [Indexed: 10/18/2022]
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Xu L, Wang J, Ding Y, Wang L, Zhu YJ. Current Knowledge of Microglia in Traumatic Spinal Cord Injury. Front Neurol 2022; 12:796704. [PMID: 35087472 PMCID: PMC8787368 DOI: 10.3389/fneur.2021.796704] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 12/07/2021] [Indexed: 12/12/2022] Open
Abstract
Microglia are the resident immune cells in the central nervous system (CNS). After traumatic spinal cord injury (SCI), microglia undergo activation, proliferation, and changes in gene and protein expression and morphology, with detrimental and beneficial effects. Activated microglia cause secondary neuronal injury via the production of proinflammatory cytokines, reactive oxygen species, and proteases. However, activated microglia also promote neuronal repair through the secretion of anti-inflammatory growth factors and cytokines. Proinflammatory cytokines increase endothelial permeability, promote A1 astrocyte activation and axonal demyelination, and reduce neural stem/progenitor cells (NSPCs), leading to the exacerbation of neuronal injury. In contrast, anti-inflammatory factors facilitate angiogenesis, reduce reactive astrocytes, and promote axonal remyelination and the propagation of NSPCs, contributing to tissue repair and locomotor recovery. Due to its limited regenerative capacity, the CNS requires beneficial microglia for continuous protection against injury. Understanding and regulating microglial activation status are beneficial to reducing detrimental effects and promoting repair behaviors and to obtain more information on efficient therapies for traumatic SCI. This review discusses microglial activation and the differences between microglia and similar immune cells, microglial interactions with other cells in the spinal cord, and the progress in the development of therapies targeting microglia in SCI.
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Affiliation(s)
- Lintao Xu
- Department of Neurosurgery, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Jingyu Wang
- Department of Neurosurgery, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Yueming Ding
- School of Medicine, Zhejiang University City College, Hangzhou, China
| | - Linlin Wang
- Department of Basic Medicine Sciences, and Department of Orthopaedics of Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yong-Jian Zhu
- Department of Neurosurgery, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
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20
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Emerging trends in BCI-robotics for motor control and rehabilitation. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2021. [DOI: 10.1016/j.cobme.2021.100354] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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21
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Deep and Wide Transfer Learning with Kernel Matching for Pooling Data from Electroencephalography and Psychological Questionnaires. SENSORS 2021; 21:s21155105. [PMID: 34372338 PMCID: PMC8347227 DOI: 10.3390/s21155105] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 07/03/2021] [Accepted: 07/09/2021] [Indexed: 12/22/2022]
Abstract
Motor imagery (MI) promotes motor learning and encourages brain–computer interface systems that entail electroencephalogram (EEG) decoding. However, a long period of training is required to master brain rhythms’ self-regulation, resulting in users with MI inefficiency. We introduce a parameter-based approach of cross-subject transfer-learning to improve the performances of poor-performing individuals in MI-based BCI systems, pooling data from labeled EEG measurements and psychological questionnaires via kernel-embedding. To this end, a Deep and Wide neural network for MI classification is implemented to pre-train the network from the source domain. Then, the parameter layers are transferred to initialize the target network within a fine-tuning procedure to recompute the Multilayer Perceptron-based accuracy. To perform data-fusion combining categorical features with the real-valued features, we implement stepwise kernel-matching via Gaussian-embedding. Finally, the paired source–target sets are selected for evaluation purposes according to the inefficiency-based clustering by subjects to consider their influence on BCI motor skills, exploring two choosing strategies of the best-performing subjects (source space): single-subject and multiple-subjects. Validation results achieved for discriminant MI tasks demonstrate that the introduced Deep and Wide neural network presents competitive performance of accuracy even after the inclusion of questionnaire data.
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22
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Gao X, Wang Y, Chen X, Gao S. Interface, interaction, and intelligence in generalized brain-computer interfaces. Trends Cogn Sci 2021; 25:671-684. [PMID: 34116918 DOI: 10.1016/j.tics.2021.04.003] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 03/07/2021] [Accepted: 04/05/2021] [Indexed: 11/16/2022]
Abstract
A brain-computer interface (BCI) establishes a direct communication channel between a brain and an external device. With recent advances in neurotechnology and artificial intelligence (AI), the brain signals in BCI communication have been advanced from sensation and perception to higher-level cognition activities. While the field of BCI has grown rapidly in the past decades, the core technologies and innovative ideas behind seemingly unrelated BCI systems have never been summarized from an evolutionary point of view. Here, we review various BCI paradigms and present an evolutionary model of generalized BCI technology which comprises three stages: interface, interaction, and intelligence (I3). We also highlight challenges, opportunities, and future perspectives in the development of new BCI technology.
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Affiliation(s)
- Xiaorong Gao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Yijun Wang
- Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
| | - Xiaogang Chen
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences, Tianjin, China
| | - Shangkai Gao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
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Sun W, Qi Y, Sun Y, Zhao T, Su X, Liu Y. Optimization of Surface Electromyography-Based Neurofeedback Rehabilitation Intervention System. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:5546716. [PMID: 33815729 PMCID: PMC7990534 DOI: 10.1155/2021/5546716] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 03/03/2021] [Accepted: 03/10/2021] [Indexed: 11/30/2022]
Abstract
In this paper, we study the effects of the neurofeedback method of surface EMG on electrophysiology and evaluate its effects on postural control, balance, and motor function using relevant scales. We optimize the neurofeedback rehabilitation intervention system based on surface EMG, study the objective assessment of neurofeedback rehabilitation intervention of surface EMG, and initially try to apply mirror therapy to the treatment of surface EMG. According to the different treatment methods, they were divided into the drug-only group, drug combined with electroacupuncture group, drug combined with facial muscle function training group, and drug combined with electroacupuncture combined with facial muscle function training group. Starting from the 10th day of the disease course, a course of 15 days contains three courses of treatment with a 3-day break for each course. Patients were tested on day 10, day 25, and day 40 of the disease course and the results of each test were recorded and analyzed. The results of each test were recorded and analyzed. The efficacy of four different methods for simple neurofeedback rehabilitation was compared according to the mean ratio of the root mean square of the patient's affected and healthy sides. The close relationship between surface EMG neurofeedback rehabilitation intervention and rehabilitation efficacy was also investigated, and the effect of different feedback modes on neurofeedback rehabilitation intervention was explored for the neurofeedback protocol and whether the use of the optimized neurorehabilitation protocol could achieve improved efficacy and have a sustained effect. The study showed that neurofeedback training interventions based on optimized surface EMG can achieve good long-term results, as demonstrated by improved postural control, balance, and motor function of patients; optimized neurofeedback rehabilitation intervention systems; and guiding physicians or nurses to work more effective clinically.
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Affiliation(s)
- Wenlin Sun
- Department of Rehabilitation Medicine, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu 223300, China
| | - Yujun Qi
- Department of Rehabilitation Medicine, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu 223300, China
| | - Yang Sun
- Department of Imaging, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu 223300, China
| | - Tiantian Zhao
- Department of Neurology I, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu 223300, China
| | - Xiaoyong Su
- Department of Rehabilitation Medicine, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu 223300, China
| | - Yang Liu
- Department of Rehabilitation Medicine, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu 223300, China
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Velasquez-Martinez L, Caicedo-Acosta J, Acosta-Medina C, Alvarez-Meza A, Castellanos-Dominguez G. Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks. Brain Sci 2020; 10:E707. [PMID: 33020435 PMCID: PMC7600302 DOI: 10.3390/brainsci10100707] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 09/21/2020] [Accepted: 09/25/2020] [Indexed: 11/21/2022] Open
Abstract
Motor Imagery (MI) promotes motor learning in activities, like developing professional motor skills, sports gestures, and patient rehabilitation. However, up to 30% of users may not develop enough coordination skills after training sessions because of inter and intra-subject variability. Here, we develop a data-driven estimator, termed Deep Regression Network (DRN), which jointly extracts and performs the regression analysis in order to assess the efficiency of the individual brain networks in practicing MI tasks. The proposed double-stage estimator initially learns a pool of deep patterns, extracted from the input data, in order to feed a neural regression model, allowing for infering the distinctiveness between subject assemblies having similar variability. The results, which were obtained on real-world MI data, prove that the DRN estimator fosters pre-training neural desynchronization and initial training synchronization to predict the bi-class accuracy response, thus providing a better understanding of the Brain-Computer Interface inefficiency of subjects.
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Affiliation(s)
- Luisa Velasquez-Martinez
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170004, Colombia; (J.C.-A.); (C.A.-M.); (A.A.-M.); (G.C.-D.)
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Peng Y, Wang Z, Wong CM, Nan W, Rosa A, Xu P, Wan F, Hu Y. Changes of EEG phase synchronization and EOG signals along the use of steady state visually evoked potential-based brain computer interface. J Neural Eng 2020; 17:045006. [DOI: 10.1088/1741-2552/ab933e] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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