AI RESEARCH
Tokenizing Single-Channel EEG with Time-Frequency Motif Learning
arXiv CS.LG
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ArXi:2502.16060v5 Announce Type: replace Foundation models are reshaping EEG analysis, yet an important problem of EEG tokenization remains a challenge. This paper presents TFM-Tokenizer, a novel tokenization framework that learns a vocabulary of time-frequency motifs from single-channel EEG signals and encodes them into discrete tokens. We propose a dual-path architecture with time-frequency masking to capture robust motif representations, and it is model-agnostic, ing both lightweight transformers and existing foundation models for downstream tasks.