Tokenizing Single-Channel EEG with Time-Frequency Motif Learning

Jathurshan Pradeepkumar, Xihao Piao, Zheng Chen, Jimeng Sun

Keywords: EEG tokenization, Self-supervised learning, EEG representation learning

TL;DR: We introduce TFM-Token, a tokenization framework for EEG that captures time–frequency motifs from single-channel signals, reducing their complexity and leading to state-of-the-art EEG analysis with fewer parameters.

Abstract: We introduce TFM-Tokenizer, a novel tokenization framework tailored for EEG analysis that transforms continuous, noisy brain signals into a sequence of discrete, well-represented tokens for various EEG tasks. Conventional approaches typically rely on continuous embeddings and inter-channel dependencies, which are limited in capturing inherent EEG features such as temporally unpredictable patterns and diverse oscillatory waveforms. In contrast, we hypothesize that critical time-frequency features can be effectively captured from a single channel. By learning tokens that encapsulate these intrinsic patterns within a single channel, our approach yields a scalable tokenizer adaptable across diverse EEG settings. We integrate the TFM-Tokenizer with a transformer-based TFM-Encoder, leveraging established pretraining techniques from natural language processing, such as masked token prediction, followed by downstream fine-tuning for various EEG tasks. Experiments across four EEG datasets show that TFM-Token outperforms state-of-the-art methods in single-dataset settings. Comprehensive analysis shows that the learned tokens capture class-specific features, preserve frequency content, and encode interpretable time–frequency motifs.

Submission Number: 99