Microstate tokenizer from clustered EEG signals provides universal representations that outperform traditional time- and frequency-domain features across sleep staging, emotion recognition, and motor imagery tasks.
Chén, Philipp Koch, Alfred Mertins, and Maarten De Vos
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Randomly initialized Transformers act as adaptive sequence smoothers for sleep staging via a Random Attention Prior Kernel, with gains mainly from inductive bias rather than training.
citing papers explorer
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Atoms of Thought: Universal EEG Representation Learning with Microstates
Microstate tokenizer from clustered EEG signals provides universal representations that outperform traditional time- and frequency-domain features across sleep staging, emotion recognition, and motor imagery tasks.
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Rethinking Random Transformers as Adaptive Sequence Smoothers for Sleep Staging
Randomly initialized Transformers act as adaptive sequence smoothers for sleep staging via a Random Attention Prior Kernel, with gains mainly from inductive bias rather than training.