Moore and More ›› 2025, Vol. 1 ›› Issue (2): 114-133.DOI: 10.1007/s44275-024-00017-w

• ORIGINAL ARTICLES • Previous Articles    

Design and implementation of a scalable and high-throughput EEG acquisition and analysis system

Haifeng Liu1,2, Zhenghang Zhu1,3, Zhenyu Wang1, Xi Zhao3,4, Tianheng Xu1,3, Ting Zhou1,3,4, Celimuge Wu6, Edison Pignaton De Freitas7, Honglin Hu1,5   

  1. 1. Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, 201203, China;
    2. School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China;
    3. Shanghai Frontier Innovation Research Institute, Shanghai, 201108, China;
    4. School of Microelectronics, Shanghai University, Shanghai, 200444, China;
    5. University of Chinese Academy of Sciences, Beijing, 100049, China;
    6. Meta-Networking Research Center, University of Electro-Communications, Tokyo, 182-8585, Japan;
    7. Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, 90040-060, Brazil
  • Received:2024-06-27 Revised:2024-10-09 Accepted:2024-10-18 Online:2024-12-09 Published:2024-12-09
  • Contact: Honglin Hu,E-mail:huhl@sari.ac.cn
  • Supported by:
    This work was supported in part by the Shanghai Pilot Program for Basic Research-Chinese Academy of Sciences, Shanghai Branch, under Grant JCYJ-SHFY-2022-0xx; in part by the Science and Technology Commission Foundation of Shanghai under Grant 21142200300; and in part by the Shanghai Industrial Collaborative Innovation Project under Grant XTCX-KJ-2023-05.

Design and implementation of a scalable and high-throughput EEG acquisition and analysis system

Haifeng Liu1,2, Zhenghang Zhu1,3, Zhenyu Wang1, Xi Zhao3,4, Tianheng Xu1,3, Ting Zhou1,3,4, Celimuge Wu6, Edison Pignaton De Freitas7, Honglin Hu1,5   

  1. 1. Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, 201203, China;
    2. School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China;
    3. Shanghai Frontier Innovation Research Institute, Shanghai, 201108, China;
    4. School of Microelectronics, Shanghai University, Shanghai, 200444, China;
    5. University of Chinese Academy of Sciences, Beijing, 100049, China;
    6. Meta-Networking Research Center, University of Electro-Communications, Tokyo, 182-8585, Japan;
    7. Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, 90040-060, Brazil
  • 通讯作者: Honglin Hu,E-mail:huhl@sari.ac.cn
  • 作者简介:Haifeng Liu received a B.S. degree in information engineering from Zhejiang University, China, in 2021. He is currently pursuing a Ph.D. degree at ShanghaiTech University, Shanghai. His research interests include wearable bioelectronics and XR-integrated braincomputer interfaces.
    Zhenghang Zhu received an M.S. degree in information and communication engineering from Tsinghua University, in 2008. His main research interests include software-defined radio, nextgeneration wireless networks, and V2X.
    Zhenyu Wang received a Ph.D. degree from the Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, China in 2021. He is currently an assistant professor with the Shanghai Advanced Research Institute, Chinese Academy of Sciences. His research interests include braincomputer interface, biological signal processing, and machine learning.
    Xi Zhao received a Ph.D. degree from the Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, China in 2023. His research interest includes brain-computer interface with focus on BCIs based on steady-state visual evoked potential for enhancing the user experience.
    Tianheng Xu received a Ph.D. degree from the Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China in 2016. He is currently an sasociate professor with the Shanghai Advanced Research Institute, Chinese Academy of Sciences. His research interests include 5G/6G wireless communications, ubiquitous cognition and intelligent spectrum sensing technology, multimodal signal processing, and heterogeneous system interaction technologies. He received the President Scholarship of Chinese Academy of Sciences (First Grade) in 2014, the Outstanding Ph.D. Graduates Award of Shanghai in 2016, best paper awards at the IEEE GLOBECOM 2016 and the Springer MONAMI 2021, the First Prize of Technological Invention Award from the China Institute of Communication, in 2019, and the First Prize of Shanghai Technological Invention Award in 2020.
    Ting Zhou received B.S. and M.S. degrees from the Department of Electronic Engineering, Tsinghua University in 2004 and 2006, respectively, and a Ph.D. degree from the Shanghai Institute of Microsystem and Information Technology (SIMIT), Chinese Academy of Sciences (CAS) in 2011. She is currently a full professor with the School of Microelectronics, Shanghai University. She has published over 70 journal articles and conference papers and holds more than 60 granted or filed patents. Her research interests include wireless resource management and mobility management, intelligent networking of heterogeneous wireless networks, 5G mobile systems, and brain-computer interface. She has won the 2020 First Prize of Shanghai Science and Technology Award, and the 2019, 2016, and 2015 First Prize of Technological Invention Awards from China Institute of Communications.
    Celimuge Wu received his Ph.D. degree from the University of Electro-Communications, Tokyo, Japan, in 2010. He is a full professor and Director of the MetaNetworking Research Center, University of Electro- Communications. His research interests include vehicular networks, internetof-things, edge computing, and application of machine learning in wireless networking. He has published more than 200 papers in reputable journals and conferences. He serves as an associate editor of IEEE Transactions on Cognitive Communications and Networking, IEEE Transactions on Network Science and Engineering, IEEE Transactions on Green Communications and Networking, and IEEE Open Journal of the Computer Society. He also has been a guest editor of IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE Computational Intelligence Magazine, etc. He is a recipient of the 2021 IEEE Communications Society Outstanding Paper Award, 2021 IEEE Internet of Things Journal Best Paper Award, IEEE Computer Society 2020 Best Paper Award, and IEEE Computer Society 2019 Best Paper Award Runner-Up. He is/has been a TPC Co-Chair of IEEE International Smart Cities Conference 2021 (https://attend. ieee.org/isc2-2021/), a TPC Co-Chair of the 2021 IEEE Autonomous Driving AI Test Challenge (http://av-test-challenge.org/index.html), a General Chair (Lead) of ICT-DM 2021 (http://ict-dm.org/), a TPC Co-Chair of Wireless Days 2021 (https://wd2021.dnac.org/), a Track Chair (Lead) of IEEE VTC-Spring 2020 (https://events.vtsociety.org/ vtc2020-spring/), a Track Chair (Lead) of ICCCN 2019 (http://www. icccn.org/icccn19/index.html), and a Track Chair of IEEE PIMRC 2016 (https://pimrc2016.ieee-pimrc.org/). He is the Chair of IEEE TCBD Special Interest Group on Big Data with Computational Intelligence and IEEE TCGCC Special Interest Group on Green Internet of Vehicles. He is a senior member of IEEE.
    Edison Pignaton De Freitas received his Bac. degree in computer engineering from the Military Institute of Engineering, Brazil in 2003, and his M.Sc. degree in computer science from the Federal University of Rio Grande do Sul (UFRGS), Brazil in 2007. He received his Ph.D. from Halmstad University, Sweden in 2011), in the area of sensor networks. During 2001-2002 he studied in France, at the Institute National des Sciences Appliquées, Toulouse, with a scholarship from the Brazilian National Counsel of Technological and Scientific Development (CNPq). Currently, he holds a position as associate professor at the Informatics Institute at UFRGS since 2014. He worked as computer engineer and researcher at the Brazilian Army from 2004 to 2013, working in several areas of computer-based defense systems, such as tactical edge networks, unmanned platforms, missile navigation, and aerospace defense. During his stay in France, he held an internship at AIRBUS Central Entity working on the A380 project, working with avionic systems requirement engineering and in the specification of the aircraft refueling system. His current main research interests are computer networks, distributed real-time and embedded systems, wireless sensor networks, avionics systems specification, artificial intelligence (multiagent systems), robotics navigation, internet of things and (multi-) unmanned aerial vehicles systems. Within these research areas, he maintains cooperation with several leading universities around the world, highlighting Linkoping Uni- ¨ versity in Sweden and Ruhr Universitat Bochum in Germany, among ¨ others.
    Honglin Hu received a Ph.D. degree in communications and information systems from the University of Science and Technology of China, China in 2004. He was with Future Radio, Siemens AG Communications, Germany. From 2015 to 2018, he was a Finland Distinguished Professor (FiDiPro) with the VTT Technical Research Centre of Finland, Finland. Since 2009, he has been a full professor with the Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, China. Since 2013, he has also been an adjunct professor with ShanghaiTech University, China, and the Vice Director of the Shanghai Research Center for Wireless Communications, China. Since 2016, he has been with the Shanghai Advanced Research Institute, Chinese Academy of Sciences. He was a recipient of the 2016 IEEE Jack Neubauer Memorial Award (the Best Paper Award of the IEEE Transactions on Vehicular Technology) and the Best Paper Award from the IEEE GlobeCom 2016. He was the Vice Chair of the IEEE Shanghai Section. He was a Leading Guest Editor of the IEEE Wireless Communications special issue on Mobile Converged Networks and the IEEE Communications Magazine special issue on Software Defined Wireless Networks (Part I and Part II).
  • 基金资助:
    This work was supported in part by the Shanghai Pilot Program for Basic Research-Chinese Academy of Sciences, Shanghai Branch, under Grant JCYJ-SHFY-2022-0xx; in part by the Science and Technology Commission Foundation of Shanghai under Grant 21142200300; and in part by the Shanghai Industrial Collaborative Innovation Project under Grant XTCX-KJ-2023-05.

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Abstract: Recent advances in neuroscience, neuromorphic intelligence, and brain–computer interface (BCI) technologies have created a need for fast, efficient, and convenient electroencephalogram (EEG) data acquisition systems. However, the existing equipment was limited in its flexibility, restricting non-invasive studies to research or medical settings. To address this issue, low-cost, compact EEG acquisition devices have been developed, allowing for frequent and flexible brain data acquisition in various scenarios. This paper introduces a scalable and high-throughput EEG signal acquisition and analysis system based on field-programmable gate array (FPGA) technology. The proposed system offers electrode scalability, on-chip computing, and optional wireless functionality extension. These features are achieved through the design of a highly scalable underlying EEG acquisition module and an FPGA central module that enables software-defined high-throughput expansion and high-speed data exchange between software and hardware. The paper presents two implementation cases that demonstrate the potential of the proposed system. The first case introduces a wearable wireless EEG system, enabling the deployment of effective and user-friendly steady-state visual evoked potential (SSVEP)-BCI applications in consumer-grade scenarios. The second case integrates an FPGA central module with multiple basic EEG acquisition modules to construct a high-throughput BCI system for cost-effective and real-time EEG data acquisition and processing. This configuration allows for flexible deployment in research and clinical applications, including attention index, SSVEP, motor imagery (MI), and emotion recognition. This combination further demonstrates the potential of scalable EEG systems and emphasizes the need for further integration or chipization. These implementations validate the feasibility of compact and efficient EEG devices and highlight the promising applications of scalable BCI system in various fields.

Key words: Electroencephalogram (EEG), Brain–computer interface (BCI), Field-programmable gate array (FPGA)

摘要： Recent advances in neuroscience, neuromorphic intelligence, and brain–computer interface (BCI) technologies have created a need for fast, efficient, and convenient electroencephalogram (EEG) data acquisition systems. However, the existing equipment was limited in its flexibility, restricting non-invasive studies to research or medical settings. To address this issue, low-cost, compact EEG acquisition devices have been developed, allowing for frequent and flexible brain data acquisition in various scenarios. This paper introduces a scalable and high-throughput EEG signal acquisition and analysis system based on field-programmable gate array (FPGA) technology. The proposed system offers electrode scalability, on-chip computing, and optional wireless functionality extension. These features are achieved through the design of a highly scalable underlying EEG acquisition module and an FPGA central module that enables software-defined high-throughput expansion and high-speed data exchange between software and hardware. The paper presents two implementation cases that demonstrate the potential of the proposed system. The first case introduces a wearable wireless EEG system, enabling the deployment of effective and user-friendly steady-state visual evoked potential (SSVEP)-BCI applications in consumer-grade scenarios. The second case integrates an FPGA central module with multiple basic EEG acquisition modules to construct a high-throughput BCI system for cost-effective and real-time EEG data acquisition and processing. This configuration allows for flexible deployment in research and clinical applications, including attention index, SSVEP, motor imagery (MI), and emotion recognition. This combination further demonstrates the potential of scalable EEG systems and emphasizes the need for further integration or chipization. These implementations validate the feasibility of compact and efficient EEG devices and highlight the promising applications of scalable BCI system in various fields.

关键词: Electroencephalogram (EEG), Brain–computer interface (BCI), Field-programmable gate array (FPGA)

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