Instrumentation, Measurement, and Signal Processing in Electroencephalography-Based Brain-Computer Interfaces: Situations and Prospects

被引:4
作者
Xue, Zifan [1 ]
Zhang, Yunfan [1 ]
Li, Hui [2 ,3 ]
Chen, Hongbin [4 ]
Shen, Shengnan [5 ,6 ]
Du, Hejun [7 ]
机构
[1] Wuhan Univ, Inst Technol Sci, Wuhan 430072, Peoples R China
[2] Wuhan Univ, Hubei Prov Engn Res Ctr Gener Technol Integrated C, Sch Power & Mech Engn, Inst Technol Sci,Hubei Key Lab Elect Mfg & Packagi, Wuhan 430072, Peoples R China
[3] Wuhan Univ, Shenzhen Res Inst, Shenzhen 518057, Peoples R China
[4] Wuhan Univ, Dept Pulm & Crit Care Med, Renmin Hosp, Wuhan 430060, Peoples R China
[5] Wuhan Univ, Sch Power & Mech Engn, Wuhan 430072, Peoples R China
[6] Wuhan Univ, Shenzhen Res Inst, Shenzhen 518057, Peoples R China
[7] Nanyang Technol Univ, Sch Mech & Aerosp Engn, Singapore 639798, Singapore
关键词
Electroencephalography; Instruments; Current measurement; Spatial resolution; Task analysis; Rhythm; Motors; Brain-computer interfaces (BCIs); electroencephalography (EEG); EEG signal measurement; EEG signal processing; instrument; MICRONEEDLE-ARRAY ELECTRODE; BLIND SOURCE SEPARATION; MAGNETORHEOLOGICAL DRAWING LITHOGRAPHY; EEG SIGNALS; SENSORIMOTOR RHYTHMS; FEATURE-EXTRACTION; MACHINE INTERFACE; DRY ELECTRODES; BCI; CLASSIFICATION;
D O I
10.1109/TIM.2024.3417598
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Proper signal measurement and processing are crucial in electroencephalography (EEG)-based brain-computer interfaces (BCIs), as they form the basis of brain insight and precise BCI control. Currently, extensive papers have reported their progress and successful applications in this field. Nevertheless, a systematic review of progress and challenges in this field is still lacking, and the research challenges have not been thoroughly discussed. Herein, a systematic review of instrumentation, measurement, and signal processing in EEG-based BCIs is proposed. First, EEG signals and the application of EEG-based BCIs are introduced. Then, the components and products related to the measurement, processing, and control of EEG signals are analyzed. Specifically, detailed discussions are provided on the measurement methods and results. Moreover, typical EEG paradigms and the processing methods of EEG signals are analyzed. Finally, four major challenges in this field are proposed and discussed: BCIs for acquiring high-quality EEG signals, EEG-based BCIs for long-term tasks, EEG-based BCIs for mobile or dynamic scenarios, and EEG-based BCIs with user-centered designs. This study offers practitioners a comprehensive guide for the measurement and processing of EEG signals, encompassing instrument selection, methodology implementation, current challenges, and future considerations.
引用
收藏
页数:28
相关论文
共 50 条
  • [41] Advances in Multimodal Emotion Recognition Based on Brain-Computer Interfaces
    He, Zhipeng
    Li, Zina
    Yang, Fuzhou
    Wang, Lei
    Li, Jingcong
    Zhou, Chengju
    Pan, Jiahui
    BRAIN SCIENCES, 2020, 10 (10) : 1 - 29
  • [42] Mind the gap: State-of-the-art technologies and applications for EEG-based brain-computer interfaces
    Portillo-Lara, Roberto
    Tahirbegi, Bogachan
    Chapman, Christopher A. R.
    Goding, Josef A.
    Green, Rylie A.
    APL BIOENGINEERING, 2021, 5 (03)
  • [43] Deep Temporal-Spatial Feature Learning for Motor Imagery-Based Brain-Computer Interfaces
    Chen, Junjian
    Yu, Zhuliang
    Gu, Zhenghui
    Li, Yuanqing
    IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2020, 28 (11) : 2356 - 2366
  • [44] Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016
    Wu, Dongrui
    Xu, Yifan
    Lu, Bao-Liang
    IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, 2022, 14 (01) : 4 - 19
  • [45] State of the Art and Future Prospects of Nanotechnologies in the Field of Brain-Computer Interfaces
    Athanasiou, A.
    Klados, M. A.
    Astaras, A.
    Foroglou, N.
    Magras, I.
    Bamidis, P. D.
    XIV MEDITERRANEAN CONFERENCE ON MEDICAL AND BIOLOGICAL ENGINEERING AND COMPUTING 2016, 2016, 57 : 456 - 460
  • [46] Advanced Modeling and Signal Processing Methods in Brain-Computer Interfaces Based on a Vector of Cyclic Rhythmically Connected Random Processes
    Lupenko, Serhii
    Butsiy, Roman
    Shakhovska, Nataliya
    SENSORS, 2023, 23 (02)
  • [47] EEG Decoding Based on Normalized Mutual Information for Motor Imagery Brain-Computer Interfaces
    Tang, Chao
    Jiang, Dongyao
    Dang, Lujuan
    Chen, Badong
    IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, 2024, 16 (06) : 1997 - 2007
  • [48] Hybrid Brain-Computer Interface Spellers: A Walkthrough Recent Advances in Signal Processing Methods and Challenges
    Chugh, Nupur
    Aggarwal, Swati
    INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION, 2023, 39 (15) : 3096 - 3113
  • [49] Stimulus Design for Visual Evoked Potential Based Brain-Computer Interfaces
    Xu, Haoyin
    Hsu, Sheng-Hsiou
    Nakanishi, Masaki
    Lin, Yufan
    Jung, Tzyy-Ping
    Cauwenberghs, Gert
    IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2023, 31 : 2545 - 2551
  • [50] Matching Pursuit for P300-based Brain-Computer Interfaces
    Vareka, Lukas
    2012 35TH INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS AND SIGNAL PROCESSING (TSP), 2012, : 513 - 516