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    Volume 1, Issue 3, Chinese Journal of Information Fusion
    Volume 1, Issue 3, 2024
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    Hongqi Fan
    National University of Defense Technology, China
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    Chinese Journal of Information Fusion, 2024, Volume 1, Issue 3: 183-211

    Code (Data) Available | Free to Read | Review Article | 15 December 2024
    A Comprehensive Survey on Emerging Techniques and Technologies in Spatio-Temporal EEG Data Analysis
    by
    IECE Author Pengfei Wang 1
    IECE Author Huanran Zheng 1
    IECE Author Silong Dai 1
    IECE Author Yiqiao Wang 1
    IECE Author Xiaotian Gu 1
    IECE Author Yuanbin Wu 1 *
    IECE Author Xiaoling Wang 1 *
    1 School of Computer Science and Technology, East China Normal University, Shanghai 200062, China
    * Corresponding Authors: Yuanbin Wu, [email protected] ; Xiaoling Wang, [email protected]
    DOI: https://doi.org/10.62762/CJIF.2024.876830
    Received: 31 July 2024, Accepted: 10 December 2024, Published: 15 December 2024  
       PDF  (3.53 MB)
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    Abstract
    In recent years, the field of electroencephalography (EEG) analysis has witnessed remarkable advancements, driven by the integration of machine learning and artificial intelligence. This survey aims to encapsulate the latest developments, focusing on emerging methods and technologies that are poised to transform our comprehension and interpretation of brain activity. The structure of this paper is organized according to the categorization within the machine learning community, with representation learning as the foundational concept that encompasses both discriminative and generative approaches. We delve into self-supervised learning methods that enable the robust representation of brain signals, which are fundamental for a variety of downstream applications. Within the realm of discriminative methods, we explore advanced techniques such as graph neural networks (GNN), foundation models, and approaches based on large language models (LLMs). On the generative front, we examine technologies that leverage EEG data to produce images or text, offering novel perspectives on brain activity visualization and interpretation. This survey provides an extensive overview of these cutting-edge techniques, their current applications, and the profound implications they hold for future research and clinical practice. The relevant literature and open-source materials have been compiled and are consistently updated at https://github.com/wpf535236337/LLMs4TS.
    Graphical Abstract
    A Comprehensive Survey on Emerging Techniques and Technologies in Spatio-Temporal EEG Data Analysis
    Keywords
    electroencephalography (EEG)
    self-supervised learning (SSL)
    graph neural networks (GNN)
    foundation models
    large language models (LLMs)
    generative models
    Code / Data
    https://github.com/wpf535236337/LLMs4TS
    Funding
    This work was supported by NSFC under grant 62136002 and 62477014, Ministry of Education Research Joint Fund Project under grant 8091B042239, and Shanghai Trusted Industry Internet Software Collaborative Innovation Center.
    Cite This Article
    APA Style
    Wang, P., Zheng, H., Dai, S., Wang, Y., Gu, X., Wu, Y, & Wang, X. (2024). A Comprehensive Survey on Emerging Techniques and Technologies in Spatio-Temporal EEG Data Analysis. Chinese Journal of Information Fusion, 1(3), 183–211. https://doi.org/10.62762/CJIF.2024.876830
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