Electroencephalography (EEG) signals exhibit significant and inherent time scales differences across individuals and tasks. Despite notable successes in decoding EEG signals in single-tasks (e.g., detection of epilepsy), where the time scales are relatively consistent, substantial differences in temporal characteristics among various tasks pose a significant challenge. To address these limitations, we propose the MuST, which stands for Multi-Scale Transformer, aiming to dynamically learn characteristics of EEG signals on different time scales. Building on the conventional Convolutional Neural Network (CNN)-Transformer model, the MuST introduces two innovations: (1) A hierarchical Transformer structure to dynamically capture global dependencies and long-range information from EEG signals at different scales. (2) A novel temporal convolutional network (TCN) module to replace the original feed forward network (FFN) module in the Transformer, effectively capturing local temporal patterns and short-term dependencies from EEG signals. To validate the performance of the MuST, we conducted experiments on five public EEG datasets with extreme time-scale differences. The experimental results on these datasets demonstrate that we have achieved an average classification accuracy of 91.69% under identical parameter settings. This surpasses the baseline EEGNet by 5.65%, highlighting its superior capability in handling multi-scale EEG signals for diverse tasks. More critically, MuST demonstrates a successful unified modeling of EEG temporal heterogeneity through mixed dataset training (epilepsy detection and sleep staging classification). This breakthrough validates our multi-scale architecture's capability to dynamically reconcile divergent neurophysiological timescales within a single model. Our code can be found at https://github.com/wisercc/MuST.
Zhao et al. (2026) studied this question.