Northwestern Polytechnical University

Researchers at Northwestern Polytechnical University propose SEDAT, a hybrid tokenizer for large EEG foundation models that folds squeeze-and-excitation spatial aggregation, data-adaptive Gaussian average filtering, instantaneous-frequency-guided segmentation and Fourier-domain resampling into a single efficient pipeline. Evaluated on 10 heterogeneous EEG datasets with four foundation models, LaBraM, EEGFormer, EEGPT and NeuroGPT, SEDAT improved classification by up to 15.3% over fixed-length windowing and by 1.2-4.6% over the next-best tokenizer, the authors report. The study was published in the Journal of Neural Engineering on September 1, 2026.

A newly registered trial tests a brain-computer interface in post-stroke rehabilitation across 4 parallel arms: full cross-domain (emotion-cognition-motor) adaptive training, integrated cognitive and motor task training, single-dimension motor training, and conventional rehabilitation. Each arm plans 22 participants, 88 in total. It enrolls patients aged 30 to 80 with a first cerebral infarction or hemorrhage 3 months to 1 year earlier, unilateral hemiplegia, and upper-limb Brunnstrom stage II to IV. The primary outcome is a motor function score; secondary outcomes include EEG motor imagery event-related synchronization/desynchronization, prefrontal theta/alpha power ratio, and EEG-fNIRS coupling.