**摘要**
Learning universal representations from electroencephalogram (EEG) signals is a cutting-edge approach in the field of neuroinformatics and brain-computer interfaces (BCIs). Conventionally, EEG is treated as a multivariate temporal signal, where time- or frequency-domain features are extracted for representation learning. This paper investigates a simple yet effective EEG representation, i.e., micr
👤 作者: Xinyang Tian, Ruitao Liu, Ziyi Ye, Siyang Xue, Xin Wang, Xuesong Chen

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🔗 **[Atoms of Thought: Universal EEG Representation Learning with Microstates](https://arxiv.org/abs/2605.20182v1)**

> Atoms of Thought: Universal EEG Representation Learning with Microstates
🏷️ 来源: ArXiv cs.AI
⏱️ 2026-05-21 08:00