BiCSP: Learning Unified EEG Representations via Bidirectional Event–State Consistency

Electroencephalography (EEG) exhibits neural dynamics at multiple levels of organization. Transient regional activity encodes fine-grained temporal events, whereas coordinated interactions across brain regions give rise to higher-level contextual brain states. Existing self-supervised EEG representation learning methods typically optimize either local temporal modeling or global contextual modeling, leaving the semantic relationship between these complementary scales largely unconstrained. We propose BiCSP, a hierarchical self-supervised framework for learning unified EEG representations through Bidirectional Cross-Scale Prediction. A learnable frequency-aware front-end first enhances neural signals in the spectral domain. Regional temporal patches are encoded into Event tokens, which are subsequently aggregated by a global attention encoder into a contextual State representation. BiCSP explicitly aligns these two representation levels through complementary latent prediction objectives: Event-to-State prediction reconstructs momentum-teacher State representations from Events, while State-to-Event prediction recovers masked Event targets from a context-isolated State together with visible Event tokens. Bidirectional local Event prediction and global-context masked prediction further regularize within-scale representation learning. After pretraining, Event and State embeddings are fused into a unified downstream representation and transferred using both linear probing and full fine-tuning. By explicitly enforcing semantic consistency across representation scales, BiCSP learns transferable EEG representations beyond conventional single-scale objectives.