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Oh E, Shin S, Kim SP. Brain-computer interface in critical care and rehabilitation. Acute Crit Care 2024; 39:24-33. [PMID: 38224957 PMCID: PMC11002623 DOI: 10.4266/acc.2023.01382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 11/08/2023] [Indexed: 01/17/2024] Open
Abstract
This comprehensive review explores the broad landscape of brain-computer interface (BCI) technology and its potential use in intensive care units (ICUs), particularly for patients with motor impairments such as quadriplegia or severe brain injury. By employing brain signals from various sensing techniques, BCIs offer enhanced communication and motor rehabilitation strategies for patients. This review underscores the concept and efficacy of noninvasive, electroencephalogram-based BCIs in facilitating both communicative interactions and motor function recovery. Additionally, it highlights the current research gap in intuitive "stop" mechanisms within motor rehabilitation protocols, emphasizing the need for advancements that prioritize patient safety and individualized responsiveness. Furthermore, it advocates for more focused research that considers the unique requirements of ICU environments to address the challenges arising from patient variability, fatigue, and limited applicability of current BCI systems outside of experimental settings.
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Affiliation(s)
- Eunseo Oh
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Korea
| | - Seyoung Shin
- Department of Mechanical Engineering, Sungkyunkwan University, Suwon, Korea
| | - Sung-Phil Kim
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Korea
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2
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Santamaría-Vázquez E, Martínez-Cagigal V, Marcos-Martínez D, Rodríguez-González V, Pérez-Velasco S, Moreno-Calderón S, Hornero R. MEDUSA©: A novel Python-based software ecosystem to accelerate brain-computer interface and cognitive neuroscience research. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 230:107357. [PMID: 36693292 DOI: 10.1016/j.cmpb.2023.107357] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 12/14/2022] [Accepted: 01/15/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Neurotechnologies have great potential to transform our society in ways that are yet to be uncovered. The rate of development in this field has increased significantly in recent years, but there are still barriers that need to be overcome before bringing neurotechnologies to the general public. One of these barriers is the difficulty of performing experiments that require complex software, such as brain-computer interfaces (BCI) or cognitive neuroscience experiments. Current platforms have limitations in terms of functionality and flexibility to meet the needs of researchers, who often need to implement new experimentation settings. This work was aimed to propose a novel software ecosystem, called MEDUSA©, to overcome these limitations. METHODS We followed strict development practices to optimize MEDUSA© for research in BCI and cognitive neuroscience, making special emphasis in the modularity, flexibility and scalability of our solution. Moreover, it was implemented in Python, an open-source programming language that reduces the development cost by taking advantage from its high-level syntax and large number of community packages. RESULTS MEDUSA© provides a complete suite of signal processing functions, including several deep learning architectures or connectivity analysis, and ready-to-use BCI and neuroscience experiments, making it one of the most complete solutions nowadays. We also put special effort in providing tools to facilitate the development of custom experiments, which can be easily shared with the community through an app market available in our website to promote reproducibility. CONCLUSIONS MEDUSA© is a novel software ecosystem for modern BCI and neurotechnology experimentation that provides state-of-the-art tools and encourages the participation of the community to make a difference for the progress of these fields. Visit the official website at https://www.medusabci.com/ to know more about this project.
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Affiliation(s)
- Eduardo Santamaría-Vázquez
- Biomedical Engineering Group (GIB), E.T.S Ingenieros de Telecomunicación, University of Valladolid, Paseo de Belén 15, Valladolid, 47011, Spain; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Spain.
| | - Víctor Martínez-Cagigal
- Biomedical Engineering Group (GIB), E.T.S Ingenieros de Telecomunicación, University of Valladolid, Paseo de Belén 15, Valladolid, 47011, Spain; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Spain.
| | - Diego Marcos-Martínez
- Biomedical Engineering Group (GIB), E.T.S Ingenieros de Telecomunicación, University of Valladolid, Paseo de Belén 15, Valladolid, 47011, Spain.
| | - Víctor Rodríguez-González
- Biomedical Engineering Group (GIB), E.T.S Ingenieros de Telecomunicación, University of Valladolid, Paseo de Belén 15, Valladolid, 47011, Spain; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Spain.
| | - Sergio Pérez-Velasco
- Biomedical Engineering Group (GIB), E.T.S Ingenieros de Telecomunicación, University of Valladolid, Paseo de Belén 15, Valladolid, 47011, Spain.
| | - Selene Moreno-Calderón
- Biomedical Engineering Group (GIB), E.T.S Ingenieros de Telecomunicación, University of Valladolid, Paseo de Belén 15, Valladolid, 47011, Spain.
| | - Roberto Hornero
- Biomedical Engineering Group (GIB), E.T.S Ingenieros de Telecomunicación, University of Valladolid, Paseo de Belén 15, Valladolid, 47011, Spain; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Spain.
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Pan J, Chen X, Ban N, He J, Chen J, Huang H. Advances in P300 brain-computer interface spellers: toward paradigm design and performance evaluation. Front Hum Neurosci 2022; 16:1077717. [PMID: 36618996 PMCID: PMC9810759 DOI: 10.3389/fnhum.2022.1077717] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 11/23/2022] [Indexed: 12/24/2022] Open
Abstract
A brain-computer interface (BCI) is a non-muscular communication technology that provides an information exchange channel for our brains and external devices. During the decades, BCI has made noticeable progress and has been applied in many fields. One of the most traditional BCI applications is the BCI speller. This article primarily discusses the progress of research into P300 BCI spellers and reviews four types of P300 spellers: single-modal P300 spellers, P300 spellers based on multiple brain patterns, P300 spellers with multisensory stimuli, and P300 spellers with multiple intelligent techniques. For each type of P300 speller, we further review several representative P300 spellers, including their design principles, paradigms, algorithms, experimental performance, and corresponding advantages. We particularly emphasized the paradigm design ideas, including the overall layout, individual symbol shapes and stimulus forms. Furthermore, several important issues and research guidance for the P300 speller were identified. We hope that this review can assist researchers in learning the new ideas of these novel P300 spellers and enhance their practical application capability.
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Affiliation(s)
- Jiahui Pan
- School of Software, South China Normal University, Guangzhou, China
| | - XueNing Chen
- School of Software, South China Normal University, Guangzhou, China
| | - Nianming Ban
- School of Software, South China Normal University, Guangzhou, China
| | - JiaShao He
- School of Software, South China Normal University, Guangzhou, China
| | - Jiayi Chen
- School of Software, South China Normal University, Guangzhou, China
| | - Haiyun Huang
- School of Software, South China Normal University, Guangzhou, China
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Bianchi L, Ferrante R, Hu Y, Sahonero-Alvarez G, Zenia NZ. Merging Brain-Computer Interface P300 speller datasets: Perspectives and pitfalls. FRONTIERS IN NEUROERGONOMICS 2022; 3:1045653. [PMID: 38235475 PMCID: PMC10790887 DOI: 10.3389/fnrgo.2022.1045653] [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: 09/15/2022] [Accepted: 11/24/2022] [Indexed: 01/19/2024]
Abstract
Background In the last decades, the P300 Speller paradigm was replicated in many experiments, and collected data were released to the public domain to allow research groups, particularly those in the field of machine learning, to test and improve their algorithms for higher performances of brain-computer interface (BCI) systems. Training data is needed to learn the identification of brain activity. The more training data are available, the better the algorithms will perform. The availability of larger datasets is highly desirable, eventually obtained by merging datasets from different repositories. The main obstacle to such merging is that all public datasets are released in various file formats because no standard way is established to share these data. Additionally, all datasets necessitate reading documents or scientific papers to retrieve relevant information, which prevents automating the processing. In this study, we thus adopted a unique file format to demonstrate the importance of having a standard and to propose which information should be stored and why. Methods We described our process to convert a dozen of P300 Speller datasets and reported the main encountered problems while converting them into the same file format. All the datasets are characterized by the same 6 × 6 matrix of alphanumeric symbols (characters and numbers or symbols) and by the same subset of acquired signals (8 EEG sensors at the same recording sites). Results and discussion Nearly a million stimuli were converted, relative to about 7000 spelled characters and belonging to 127 subjects. The converted stimuli represent the most extensively available platform for training and testing new algorithms on the specific paradigm - the P300 Speller. The platform could potentially allow exploring transfer learning procedures to reduce or eliminate the time needed for training a classifier to improve the performance and accuracy of such BCI systems.
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Affiliation(s)
- Luigi Bianchi
- Dipartimento di Ingegneria Civile ed Ingegneria Informatica, Tor Vergata University, Rome, Italy
| | - Raffaele Ferrante
- Dipartimento di Ingegneria Civile ed Ingegneria Informatica, Tor Vergata University, Rome, Italy
| | - Yaoping Hu
- Department of Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada
| | - Guillermo Sahonero-Alvarez
- Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Nusrat Z. Zenia
- Department of Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada
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Santamaría-Vázquez E, Martínez-Cagigal V, Pérez-Velasco S, Marcos-Martínez D, Hornero R. Robust asynchronous control of ERP-Based brain-Computer interfaces using deep learning. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 215:106623. [PMID: 35030477 DOI: 10.1016/j.cmpb.2022.106623] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 12/11/2021] [Accepted: 01/04/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Brain-computer interfaces (BCI) based on event-related potentials (ERP) are a promising technology for alternative and augmented communication in an assistive context. However, most approaches to date are synchronous, requiring the intervention of a supervisor when the user wishes to turn his attention away from the BCI system. In order to bring these BCIs into real-life applications, a robust asynchronous control of the system is required through monitoring of user attention. Despite the great importance of this limitation, which prevents the deployment of these systems outside the laboratory, it is often overlooked in research articles. This study was aimed to propose a novel method to solve this problem, taking advantage of deep learning for the first time in this context to overcome the limitations of previous strategies based on hand-crafted features. METHODS The proposed method, based on EEG-Inception, a novel deep convolutional neural network, divides the problem in 2 stages to achieve the asynchronous control: (i) the model detects user's control state, and (ii) decodes the command only if the user is attending to the stimuli. Additionally, we used transfer learning to reduce the calibration time, even exploring a calibration-less approach. RESULTS Our method was evaluated with 22 healthy subjects, analyzing the impact of the calibration time and number of stimulation sequences on the system's performance. For the control state detection stage, we report average accuracies above 91% using only 1 sequence of stimulation and 30 calibration trials, reaching a maximum of 96.95% with 15 sequences. Moreover, our calibration-less approach also achieved suitable results, with a maximum accuracy of 89.36%, showing the benefits of transfer learning. As for the overall asynchronous system, which includes both stages, the maximum information transfer rate was 35.54 bpm, a suitable value for high-speed communication. CONCLUSIONS The proposed strategy achieved higher performance with less calibration trials and stimulation sequences than former approaches, representing a promising step forward that paves the way for more practical applications of ERP-based spellers.
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Affiliation(s)
- Eduardo Santamaría-Vázquez
- Biomedical Engineering Group, E.T.S Ingenieros de Telecomunicación, University of Valladolid, Paseo de Belén 15, 47011, Valladolid, Spain; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Spain.
| | - Víctor Martínez-Cagigal
- Biomedical Engineering Group, E.T.S Ingenieros de Telecomunicación, University of Valladolid, Paseo de Belén 15, 47011, Valladolid, Spain; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Spain.
| | - Sergio Pérez-Velasco
- Biomedical Engineering Group, E.T.S Ingenieros de Telecomunicación, University of Valladolid, Paseo de Belén 15, 47011, Valladolid, Spain.
| | - Diego Marcos-Martínez
- Biomedical Engineering Group, E.T.S Ingenieros de Telecomunicación, University of Valladolid, Paseo de Belén 15, 47011, Valladolid, Spain.
| | - Roberto Hornero
- Biomedical Engineering Group, E.T.S Ingenieros de Telecomunicación, University of Valladolid, Paseo de Belén 15, 47011, Valladolid, Spain; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Spain.
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Li M, Zhang P, Yang G, Xu G, Guo M, Liao W. A fisher linear discriminant analysis classifier fused with naïve Bayes for simultaneous detection in an asynchronous brain-computer interface. J Neurosci Methods 2022; 371:109496. [DOI: 10.1016/j.jneumeth.2022.109496] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 01/10/2022] [Accepted: 02/06/2022] [Indexed: 11/16/2022]
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Zheng L, Pei W, Gao X, Zhang L, Wang Y. A high-performance brain switch based on code-modulated visual evoked potentials. J Neural Eng 2022; 19. [PMID: 34996051 DOI: 10.1088/1741-2552/ac494f] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 01/07/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Asynchronous brain-computer interfaces (BCIs) are more practical and natural compared to synchronous BCIs. A brain switch is a standard asynchronous BCI, which can automatically detect the specified change of the brain and discriminate between the control state and the idle state. The current brain switches still face challenges on relatively long reaction time (RT) and high false positive rate (FPR). APPROACH In this paper, an online electroencephalography-based brain switch is designed to realize a fast reaction and keep long idle time (IDLE) without false positives (FPs) using code-modulated visual evoked potentials (c-VEPs). Two stimulation paradigms were designed and compared in the experiments: multi-code concatenate modulation (concatenation mode) and single-code periodic modulation (periodic mode). Using a task-related component analysis-based detection algorithm, EEG data can be decoded into a series of code indices. Brain states can be detected by a template matching approach with a sliding window on the output series. MAIN RESULTS The online experiments achieved an average RT of 1.49 seconds when the average IDLE for each FP was 68.57 minutes (1.46e-2 FP/min) or an average RT of 1.67 seconds without FPs. SIGNIFICANCE This study provides a practical c-VEP based brain switch system with both fast reaction and low FPR during idle state, which can be used in various BCI applications.
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Affiliation(s)
- Li Zheng
- State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, No.A35, QingHua East Road, Institute of Semiconductors , CAS, Haidian District, Beijing, 100083, CHINA
| | - Weihua Pei
- State Key Laboratory of Integrated Optoelectronics, Chinese Academy of Sciences - Institute of Semiconductors, PO Box 912, Beijing 100083, Beijing, 100083, CHINA
| | - Xiaorong Gao
- Department of Biomedical Engineering School of Medicine, Tsinghua University, Beijing 100084, PR CHINA, Beijing, 100084, CHINA
| | - Lijian Zhang
- Beijing Institute of Mechanical Equipment, No. 50 Yongding Road, Haidian District, Beijing, China, Beijing, 100854, CHINA
| | - Yijun Wang
- State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, No.A35, QingHua East Road, Institute of Semiconductors , CAS, Haidian District, Beijing, 100083, CHINA
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8
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Martínez-Cagigal V, Thielen J, Santamaría-Vázquez E, Pérez-Velasco S, Desain P, Hornero R. Brain-computer interfaces based on code-modulated visual evoked potentials (c-VEP): a literature review. J Neural Eng 2021; 18. [PMID: 34763331 DOI: 10.1088/1741-2552/ac38cf] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 11/11/2021] [Indexed: 11/11/2022]
Abstract
Objective.Code-modulated visual evoked potentials (c-VEP) have been consolidated in recent years as robust control signals capable of providing non-invasive brain-computer interfaces (BCIs) for reliable, high-speed communication. Their usefulness for communication and control purposes has been reflected in an exponential increase of related articles in the last decade. The aim of this review is to provide a comprehensive overview of the literature to gain understanding of the existing research on c-VEP-based BCIs, since its inception (1984) until today (2021), as well as to identify promising future research lines.Approach.The literature review was conducted according to the Preferred Reporting Items for Systematic reviews and Meta-Analysis guidelines. After assessing the eligibility of journal manuscripts, conferences, book chapters and non-indexed documents, a total of 70 studies were included. A comprehensive analysis of the main characteristics and design choices of c-VEP-based BCIs was discussed, including stimulation paradigms, signal processing, modeling responses, applications, etc.Main results.The literature review showed that state-of-the-art c-VEP-based BCIs are able to provide an accurate control of the system with a large number of commands, high selection speeds and even without calibration. In general, a lack of validation in real setups was observed, especially regarding the validation with disabled populations. Future work should be focused toward developing self-paced c-VEP-based portable BCIs applied in real-world environments that could exploit the unique benefits of c-VEP paradigms. Some aspects such as asynchrony, unsupervised training, or code optimization still require further research and development.Significance.Despite the growing popularity of c-VEP-based BCIs, to the best of our knowledge, this is the first literature review on the topic. In addition to providing a joint discussion of the advances in the field, some future lines of research are suggested to contribute to the development of reliable plug-and-play c-VEP-based BCIs.
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Affiliation(s)
- Víctor Martínez-Cagigal
- Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, Paseo de Belén, 15, University of Valladolid, Valladolid, Spain.,Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Jordy Thielen
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Eduardo Santamaría-Vázquez
- Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, Paseo de Belén, 15, University of Valladolid, Valladolid, Spain.,Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Sergio Pérez-Velasco
- Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, Paseo de Belén, 15, University of Valladolid, Valladolid, Spain
| | - Peter Desain
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Roberto Hornero
- Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, Paseo de Belén, 15, University of Valladolid, Valladolid, Spain.,Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
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Chen L, Chen P, Zhao S, Luo Z, Chen W, Pei Y, Zhao H, Jiang J, Xu M, Yan Y, Yin E. Adaptive asynchronous control system of robotic arm based on augmented reality-assisted brain-computer interface. J Neural Eng 2021; 18. [PMID: 34654000 DOI: 10.1088/1741-2552/ac3044] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 10/15/2021] [Indexed: 11/12/2022]
Abstract
Objective. Brain-controlled robotic arms have shown broad application prospects with the development of robotics, science and information decoding. However, disadvantages, such as poor flexibility restrict its wide application.Approach. In order to alleviate these drawbacks, this study proposed a robotic arm asynchronous control system based on steady-state visual evoked potential (SSVEP) in an augmented reality (AR) environment. In the AR environment, the participants were able to concurrently see the robot arm and visual stimulation interface through the AR device. Therefore, there was no need to switch attention frequently between the visual stimulation interface and the robotic arm. This study proposed a multi-template algorithm based on canonical correlation analysis and task-related component analysis to identify 12 targets. An optimization strategy based on dynamic window was adopted to adjust the duration of visual stimulation adaptively.Main results. Experimental results of this study found that the high-frequency SSVEP-based brain-computer interface (BCI) realized the switch of the system state, which controlled the robotic arm asynchronously. The average accuracy of the offline experiment was 94.97%, whereas the average information translate rate was 67.37 ± 14.27 bits·min-1. The online results from ten healthy subjects showed that the average selection time of a single online command was 2.04 s, which effectively reduced the visual fatigue of the subjects. Each subject could quickly complete the puzzle task.Significance. The experimental results demonstrated the feasibility and potential of this human-computer interaction strategy and provided new ideas for BCI-controlled robots.
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Affiliation(s)
- Lingling Chen
- School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300130, People's Republic of China.,Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology Ministry of Education, Tianjin 300130, People's Republic of China
| | - Pengfei Chen
- School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300130, People's Republic of China.,Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology Ministry of Education, Tianjin 300130, People's Republic of China.,Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, People's Republic of China
| | - Shaokai Zhao
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, People's Republic of China.,Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, People's Republic of China
| | - Zhiguo Luo
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, People's Republic of China.,Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, People's Republic of China
| | - Wei Chen
- National Research Center for Rehabilitation Technical Aids, Beijing 100176, People's Republic of China
| | - Yu Pei
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, People's Republic of China
| | - Hongyu Zhao
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, People's Republic of China.,East China University of Science and Technology, Shanghai 200237, People's Republic of China
| | - Jing Jiang
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing 100094, People's Republic of China
| | - Minpeng Xu
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, People's Republic of China.,Tianjin University, Tianjin 300072, People's Republic of China
| | - Ye Yan
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, People's Republic of China.,Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, People's Republic of China
| | - Erwei Yin
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100071, People's Republic of China.,Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, People's Republic of China
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Zhou X, Xu M, Xiao X, Wang Y, Jung TP, Ming D. Detection of fixation points using a small visual landmark for brain-computer interfaces. J Neural Eng 2021; 18. [PMID: 34130268 DOI: 10.1088/1741-2552/ac0b51] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 06/15/2021] [Indexed: 11/12/2022]
Abstract
Objective.The speed of visual brain-computer interfaces (v-BCIs) has been greatly improved in recent years. However, the traditional v-BCI paradigms require users to directly gaze at the intensive flickering items, which would cause severe problems such as visual fatigue and excessive visual resource consumption in practical applications. Therefore, it is imperative to develop a user-friendly v-BCI.Approach.According to the retina-cortical relationship, this study developed a novel BCI paradigm to detect the fixation point of eyes using a small visual stimulus that subtended only 0.6° in visual angle and was out of the central visual field. Specifically, the visual stimulus was treated as a landmark to judge the eccentricity and polar angle of the fixation point. Sixteen different fixation points were selected around the visual landmark, i.e. different combinations of two eccentricities (2° and 4°) and eight polar angles (0,π4,π2,3π4,π,5π4,3π2and7π4). Twelve subjects participated in this study, and they were asked to gaze at one out of the 16 points for each trial. A multi-class discriminative canonical pattern matching (Multi-DCPM) algorithm was proposed to decode the user's fixation point.Main results.We found the visual stimulation landmark elicited different spatial event-related potential patterns for different fixation points. Multi-DCPM could achieve an average accuracy of 66.2% with a standard deviation of 15.8% for the classification of the sixteen fixation points, which was significantly higher than traditional algorithms (p⩽0.001). Experimental results of this study demonstrate the feasibility of using a small visual stimulus as a landmark to track the relative position of the fixation point.Significance.The proposed new paradigm provides a potential approach to alleviate the problem of irritating stimuli in v-BCIs, which can broaden the applications of BCIs.
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Affiliation(s)
- Xiaoyu Zhou
- The Laboratory of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, People's Republic of China
| | - Minpeng Xu
- The Laboratory of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, People's Republic of China.,The Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China
| | - Xiaolin Xiao
- The Laboratory of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, People's Republic of China.,The Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China
| | - Yijun Wang
- The State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Tzyy-Ping Jung
- The Laboratory of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, People's Republic of China.,The Swartz Center for Computational Neuroscience, University of California, San Diego, CA, United States of America
| | - Dong Ming
- The Laboratory of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, People's Republic of China.,The Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China
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Ge S, Jiang Y, Zhang M, Wang R, Iramina K, Lin P, Leng Y, Wang H, Zheng W. SSVEP-Based Brain-Computer Interface With a Limited Number of Frequencies Based on Dual-Frequency Biased Coding. IEEE Trans Neural Syst Rehabil Eng 2021; 29:760-769. [PMID: 33852388 DOI: 10.1109/tnsre.2021.3073134] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
How to encode as many targets as possible with a limited-frequency resource is a difficult problem in the practical use of a steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) speller. To solve this problem, this study developed a novel method called dual-frequency biased coding (DFBC) to tag targets in a SSVEP-based 48-character virtual speller, in which each target is encoded with a permutation sequence consisting of two permuted flickering periods that flash at different frequencies. The proposed paradigm was validated by 11 participants in an offline experiment and 7 participants in an online experiment. Three occipital channels (O1, Oz, and O2) were used to obtain the SSVEP signals for identifying the targets. Based on the coding characteristics of the DFBC method, the proposed approach has the ability of self-correction and thus achieves an accuracy of 76.6% and 79.3% for offline and online experiments, respectively, which outperforms the traditional multiple frequencies sequential coding (MFSC) method. This study demonstrates that DFBC is an efficient method for coding a high number of SSVEP targets with a small number of available frequencies.
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Li M, He D, Li C, Qi S. Brain-Computer Interface Speller Based on Steady-State Visual Evoked Potential: A Review Focusing on the Stimulus Paradigm and Performance. Brain Sci 2021; 11:450. [PMID: 33916189 PMCID: PMC8065759 DOI: 10.3390/brainsci11040450] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 03/25/2021] [Accepted: 03/29/2021] [Indexed: 11/17/2022] Open
Abstract
The steady-state visual evoked potential (SSVEP), measured by the electroencephalograph (EEG), has high rates of information transfer and signal-to-noise ratio, and has been used to construct brain-computer interface (BCI) spellers. In BCI spellers, the targets of alphanumeric characters are assigned different visual stimuli and the fixation of each target generates a unique SSVEP. Matching the SSVEP to the stimulus allows users to select target letters and numbers. Many BCI spellers that harness the SSVEP have been proposed over the past two decades. Various paradigms of visual stimuli, including the procedure of target selection, layout of targets, stimulus encoding, and the combination with other triggering methods are used and considered to influence on the BCI speller performance significantly. This paper reviews these stimulus paradigms and analyzes factors influencing their performance. The fundamentals of BCI spellers are first briefly described. SSVEP-based BCI spellers, where only the SSVEP is used, are classified by stimulus paradigms and described in chronological order. Furthermore, hybrid spellers that involve the use of the SSVEP are presented in parallel. Factors influencing the performance and visual fatigue of BCI spellers are provided. Finally, prevailing challenges and prospective research directions are discussed to promote the development of BCI spellers.
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Affiliation(s)
- Minglun Li
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China; (M.L.); (D.H.); (C.L.)
| | - Dianning He
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China; (M.L.); (D.H.); (C.L.)
| | - Chen Li
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China; (M.L.); (D.H.); (C.L.)
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China; (M.L.); (D.H.); (C.L.)
- Engineering Research Center of Medical Imaging and Intelligent Analysis, Ministry of Education, Northeastern University, Shenyang 110169, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang 110169, China
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Santamaria-Vazquez E, Martinez-Cagigal V, Vaquerizo-Villar F, Hornero R. EEG-Inception: A Novel Deep Convolutional Neural Network for Assistive ERP-Based Brain-Computer Interfaces. IEEE Trans Neural Syst Rehabil Eng 2021; 28:2773-2782. [PMID: 33378260 DOI: 10.1109/tnsre.2020.3048106] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
In recent years, deep-learning models gained attention for electroencephalography (EEG) classification tasks due to their excellent performance and ability to extract complex features from raw data. In particular, convolutional neural networks (CNN) showed adequate results in brain-computer interfaces (BCI) based on different control signals, including event-related potentials (ERP). In this study, we propose a novel CNN, called EEG-Inception, that improves the accuracy and calibration time of assistive ERP-based BCIs. To the best of our knowledge, EEG-Inception is the first model to integrate Inception modules for ERP detection, which combined efficiently with other structures in a light architecture, improved the performance of our approach. The model was validated in a population of 73 subjects, of which 31 present motor disabilities. Results show that EEG-Inception outperforms 5 previous approaches, yielding significant improvements for command decoding accuracy up to 16.0%, 10.7%, 7.2%, 5.7% and 5.1% in comparison to rLDA, xDAWN + Riemannian geometry, CNN-BLSTM, DeepConvNet and EEGNet, respectively. Moreover, EEG-Inception requires very few calibration trials to achieve state-of-the-art performances taking advantage of a novel training strategy that combines cross-subject transfer learning and fine-tuning to increase the feasibility of this approach for practical use in assistive applications.
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Ruiz-Gómez SJ, Hornero R, Poza J, Santamaría-Vázquez E, Rodríguez-González V, Maturana-Candelas A, Gomez C. A new method to build multiplex networks using Canonical Correlation Analysis for the characterization of the Alzheimer's disease continuum. J Neural Eng 2021; 18. [PMID: 33395667 DOI: 10.1088/1741-2552/abd82c] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 01/04/2021] [Indexed: 01/21/2023]
Abstract
OBJECTIVE The aim of this study was to solve one of the current limitations for the characterization of the brain network in the Alzheimer's disease (AD) continuum. Nowadays, frequency-dependent approaches have reached contradictory results depending on the frequency band under study, tangling the possible clinical interpretations. APPROACH To overcome this issue, we proposed a new method to build multiplex networks based on canonical correlation analysis (CCA). Our method determines two basis vectors using the source and electrodelevel frequency-specific network parameters for a reference group, and then project the results for the rest of the groups into these hyperplanes to make them comparable. It was applied to: (i) synthetic signals generated with a Kuramoto-based model; and (ii) a resting-state EEG database formed by recordings from 51 cognitively healthy controls, 51 mild cognitive impairment subjects, 51 mild AD patients, 50 moderate AD patients, and 50 severe AD patients. MAIN RESULTS Our results using synthetic signals showed that the interpretation of the proposed CCA-based multiplex parameters (multiplex average node degree, multiplex characteristic path length and multiplex clustering coefficient) can be analogous to their frequency-specific counterparts, as they displayed similar behaviors in terms of average connectivity, integration, and segregation. Findings using real EEG recordings revealed that dementia due to AD is characterized by a significant increase in average connectivity, and by a loss of integration and segregation. SIGNIFICANCE We can conclude that CCA can be used to build multiplex networks based from frequency-specific results, summarizing all the available information and avoiding the limitations of possible frequency-specific conflicts. Additionally, our method supposes a novel approach for the construction and analysis of multiplex networks during AD continuum.
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Affiliation(s)
- Saúl J Ruiz-Gómez
- Teoría de la señal y comunicaciones e Ingeniería telemática, Universidad de Valladolid, Paseo de Belén 15, Valladolid, Valladolid, 47011, SPAIN
| | - Roberto Hornero
- Biomedical Engineering Group, Universidad de Valladolid, ETSI Telecomunicacion, Paseo Belen 15, Valladolid, 47011, SPAIN
| | - Jesus Poza
- Communications and Signal Theory, Universidad de Valladolid, E.T.S. Ingenieros de Telecomunicacion, Paseo de Belen 15, 47011 - Valladolid, Valladolid, Valladolid, 47011, SPAIN
| | - Eduardo Santamaría-Vázquez
- Teoría de la Señal y Comunicaciones e Ingeniería Telemática, Universidad de Valladolid, Plaza de Santa Cruz, 8, Valladolid, Valladolid, 47002, SPAIN
| | - Víctor Rodríguez-González
- Teoría de la señal y comunicaciones e ingeniería telemática, Universidad de Valladolid, Paseo Belen 15, Valladolid, 47011, SPAIN
| | | | - Carlos Gomez
- Grupo de Ingeniería Biomédica, Universidad de Valladolid, E. T. S. Ingenieros de Telecomunicación, Universidad de Valladolid, Paseo Belén, 15, Valladolid, Valladolid, 47011, SPAIN
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