AI RESEARCH
Atoms of Thought: Universal EEG Representation Learning with Microstates
arXiv CS.AI
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ArXi:2605.20182v1 Announce Type: cross 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.