Cross-region neural signal reconstruction to lift electrode placement constraints in SSVEP brain-computer interfaces

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) rely on occipital EEG recordings, which are infeasible in many clinical scenarios (e.g., supine positioning, traumatic brain injury), restricting SSVEP-BCI access for patients with urgent communication needs. We propose dynamic sliding point-wise reconstruction with a triple-band cross-fusion network (DSTF-Net), a cross-brain-region framework enabling accurate SSVEP decoding exclusively from frontal EEG signals. DSTF-Net integrates four core innovations: ①a dynamic sliding point-wise reconstruction strategy that abandons static n-to-m mapping (directly mapping n frontal EEG samples to m occipital samples) to capture fine-grained temporal dependencies; ②a triple-band cross-fusion sub-network processing frontal EEG frequency bands through parallel convolutional streams and cyclic cross-attention; ③a stage-wise hierarchical training mechanism mitigating gradient interference by sequentially training single-band streams before fusion optimization; and ④a neurophysiologically constrained loss function enforcing validated unidirectional occipital-to-frontal SSVEP propagation. We trained DSTF-Net using paired frontal-occipital EEG from a healthy participant, then transferred it to 20 new users (including 8 brain-injured patients maintaining a supine position) via cross-subject transfer, reconstructing occipital activity solely from frontal EEG. Our framework achieves a maximum 33.47% decoding accuracy improvement over baselines. By eliminating occipital electrode requirements, our work expands SSVEP-BCI accessibility for clinically constrained populations and establishes a generalizable cross-brain-region neural mapping framework.