Muhammad Zulkifal Aziz

SEDAT: a hybrid tokenizer for large EEG models

Objective. The fidelity of neural representations learned by large EEG foundation models depends on how raw brain signals are tokenized. Existing methods suffer from arbitrary temporal boundaries misaligned with neural state transitions, neglecting inter-channel spatial information, and fixed segmentation criteria that...