Atoms of Thought: Universal EEG Representation Learning with Microstates
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866917511788756992 |
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| author | Tian, Xinyang Liu, Ruitao Ye, Ziyi Xue, Siyang Wang, Xin Chen, Xuesong |
| author_facet | Tian, Xinyang Liu, Ruitao Ye, Ziyi Xue, Siyang Wang, Xin Chen, Xuesong |
| contents | 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., microstates. Microstates represent the building blocks of brain activity patterns at a microscopic time scale. We build a universal microstate tokenizer from a large medical EEG dataset by clustering continuous EEG signals into sequences of discrete microstates. The microstate tokenizer is then adopted universally across a series of downstream tasks, including sleep staging, emotion recognition, and motor imagery classification. Experimental results show that EEG representation learning with microstates outperforms traditional time-domain and frequency-domain features under different models and across different tasks. Further analysis shows that microstates offer greater interpretability and scalability, thereby opening up applications in both cognitive neuroscience and clinical research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_20182 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Atoms of Thought: Universal EEG Representation Learning with Microstates Tian, Xinyang Liu, Ruitao Ye, Ziyi Xue, Siyang Wang, Xin Chen, Xuesong Machine Learning Artificial Intelligence 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., microstates. Microstates represent the building blocks of brain activity patterns at a microscopic time scale. We build a universal microstate tokenizer from a large medical EEG dataset by clustering continuous EEG signals into sequences of discrete microstates. The microstate tokenizer is then adopted universally across a series of downstream tasks, including sleep staging, emotion recognition, and motor imagery classification. Experimental results show that EEG representation learning with microstates outperforms traditional time-domain and frequency-domain features under different models and across different tasks. Further analysis shows that microstates offer greater interpretability and scalability, thereby opening up applications in both cognitive neuroscience and clinical research. |
| title | Atoms of Thought: Universal EEG Representation Learning with Microstates |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2605.20182 |