SQ-HAF: Source Quality-Guided Hierarchical Adaptation and Multi-Branch Fusion for Few-Shot Cross-Subject SSVEP Recognition.

Reducing user-specific calibration is critical for practical steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), yet few-shot cross-subject decoding remains challenged by heterogeneous source transferability and inter-subject variability. We propose SQ-HAF, a source quality-guided framework that combines target-relevant source selection, frequency neighborhood-regularized spatial filtering, covariance alignment, and multi-branch decision fusion. Candidate source subjects are ranked primarily by target-source template similarity; when more than one labeled calibration trial per stimulus is available, split-half template consistency (STC) provides a bounded confidence adjustment. The retained source data are used to construct aligned generalized and source-specific templates. For recognition, SQ-HAF fuses a five-subband harmonic reference CCA score with generalized source template and source-specific template scores. We evaluated SQ-HAF using leave-one-subject-out validation on the 35-subject Benchmark and 70-subject BETA datasets. Under the 1.0 s protocol with one labeled calibration trial per stimulus, the complete SQ-HAF configuration achieved 81.57% accuracy (ACC) and 227.83 bits/min information transfer rate (ITR) on Benchmark, and 65.56% ACC and 163.42 bits/min ITR on BETA. In a matched 0.5 s analysis with two calibration trials per stimulus, the five-subband harmonic reference design improved ACC over single-band processing on both datasets after Holm correction. These results indicate that target-relevant source screening and complementary harmonic/template evidence can support low-calibration cross-subject SSVEP decoding.