mlsanityData to 7 Oct 2026

A Survey of Spatio-Temporal EEG data Analysis: from Models to Applications

arXiv 2410.08224 ↗First posted 26 Sep 2024eess.SP cs.AI cs.LGcited by 3

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Abstract (arXiv)

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. We delve into self-supervised learning methods that enable the robust representation of brain signals, which are fundamental for a variety of downstream applications. We also explore emerging discriminative methods, including graph neural networks (GNN), foundation models, and large language models (LLMs)-based approaches. Furthermore, we examine generative technologies that harness EEG data to produce images or text, offering novel perspectives on brain activity visualization and interpretation. The 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 being refreshed at urlhttps://github.com/wpf535236337/LLMs4TS

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 94 of its 160 references that are in the map. First posted on arXiv 26 Sep 2024.

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94 of 160 references.

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Cited by year

Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2025Uni-NTFM: A Unified Foundation Model for EEG Signal Representation Learningcited by 9
  2. 2024Graph Adapter of EEG Foundation Models for Parameter Efficient Fine Tuningcited by 4
  3. 2025EEG2TEXT-CN: An Exploratory Study of Open-Vocabulary Chinese Text-EEG Alignment via Large Language Model and Contrastive Learning on ChineseEEGcited by 3