BraSTORM: A Dual-Branch Self-Supervised Framework for EEG Representation Learning via Input-Level Spatio-Temporal Decomposition
Prevalent pre-training strategies for Brain-Computer Interfaces (BCIs) are often constrained by spatio-temporal entanglement. This critical issue arises from processing multi-channel Electroencephalography (EEG) signals as monolithic sequences, which intertwines the signal