# Atoms of Thought: Universal EEG Representation Learning with Microstates

arXiv: https://arxiv.org/abs/2605.20182 · HTML: https://mlsanity.com/paper/2605.20182/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 81 references that are in the map.
- First posted on arXiv 19 May 2026.

## Builds on (most cited first, 14 of 14)

- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Subject-Aware Contrastive Learning for Biosignals](https://mlsanity.com/paper/2007.04871/) (2020)
- [Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition](https://mlsanity.com/paper/2109.09559/) (2021)
- [EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications](https://mlsanity.com/paper/2001.11337/) (2020)
- [Neural Memory Networks for Seizure Type Classification](https://mlsanity.com/paper/1912.04968/) (2019)
- [Generalizable Sleep Staging via Multi-Level Domain Alignment](https://mlsanity.com/paper/2401.05363/) (2023)
- [Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts](https://mlsanity.com/paper/2209.11233/) (2022)
- [REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates](https://mlsanity.com/paper/2406.16906/) (2024)
- [4D Attention-based Neural Network for EEG Emotion Recognition](https://mlsanity.com/paper/2101.05484/) (2021)
- [SLEEPER: interpretable Sleep staging via Prototypes from Expert Rules](https://mlsanity.com/paper/1910.06100/) (2019)
- [Automated Sleep Staging via Parallel Frequency-Cut Attention](https://mlsanity.com/paper/2204.03173/) (2022)
- [ScalingNet: extracting features from raw EEG data for emotion recognition](https://mlsanity.com/paper/2105.13987/) (2021)
- [Motor Imagery Classification of Single-Arm Tasks Using Convolutional Neural Network based on Feature Refining](https://mlsanity.com/paper/2002.01122/) (2020)
- [SleepNetZero: Zero-Burden Zero-Shot Reliable Sleep Staging With Neural Networks Based on Ballistocardiograms](https://mlsanity.com/paper/2410.22646/) (2024)

## Abstract (arXiv)

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.