BrainState: Graph-based State-Aware Generalization for Non-invasive Brain-to-Text

EEG-based brain-computer interfaces (BCIs) offer a promising pathway for neural language decoding and human-machine interaction. Yet, their generalization is limited by heterogeneous distribution shifts across recordings. Existing transfer learning methods often seek task-relevant representations that are less sensitive to physiological and recording differences. Nevertheless, state-dependent variation can persist in non-stationary EEG and affect how information is transferred across subjects. To address this, we propose BrainState, a human state aware state-space-based hierarchical graph transfer learning framework for EEG-to-language. BrainState first combines spatiotemporal convolutions with Mamba blocks to capture local EEG patterns and temporal dependencies, mapping each trial to task-relevant content and recording-state representations. It then organizes training examples into a hierarchical graph memory according to class, subject, and session, enabling condition-specific modeling and state-aware retrieval. Finally, an attention-weighted memory readout aggregates relevant prototypes, while compatibility-based retrieval and gated fusion limit contributions from poorly matched recording conditions. BrainState thereby provides a way to incorporate recording state into cross-subject EEG speech decoding. Comprehensive experiments demonstrate the SOTA performance of the framework against strong baselines under cross-subject settings. Our code is available online at https://anonymous.4open.science/r/BrainState.