Single-Channel EEG Tokenization Through Time-Frequency Modeling

• URL: http://arxiv.org/abs/2502.16060v1
• Date: Sat, 22 Feb 2025 03:32:36 GMT
• Title: Single-Channel EEG Tokenization Through Time-Frequency Modeling
• Authors: Jathurshan Pradeepkumar, Xihao Piao, Zheng Chen, Jimeng Sun,
• Abstract summary: We introduce TFM-Tokenizer, a novel tokenization framework tailored for EEG analysis.<n>By learning tokens that encapsulate intrinsic patterns within a single channel, our approach yields a scalable tokenizer across diverse EEG settings.
• Score: 27.109527161042813
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. On TUEV, our approach improves balanced accuracy and Cohen's Kappa by 5% over baselines. Comprehensive analysis of the learned tokens demonstrates their ability to capture class-distinctive features, enhance frequency representation, and ability to encode time-frequency motifs into distinct tokens, improving interpretability.

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