Atoms of Thought: Universal EEG Representation Learning with Microstates

This paper proposes using EEG microstates as discrete tokenized representations for universal brain-computer interface tasks including sleep staging, emotion recognition, and motor imagery classification. The microstate tokenizer is built via clustering on a large medical EEG dataset and outperforms time- and frequency-domain baselines across multiple downstream tasks.