HarmTSFormer: Harmonic Encoding and Evidence-Preserving Fusion for Unseen-Frequency SSVEP Decoding

Brain-computer interfaces based on steady-state visual evoked potentials (SSVEPs) identify attended flashing targets from electroencephalography (EEG) recordings. Decoders typically restrict their output to specific stimulus frequency categories, making it difficult to directly extend their performance to stimulus frequencies not encountered during training or not defined in the model. Although previous studies support unseen-frequency decoding, capturing within-candidate response structure and using information across candidates remain challenges for reliable cross-subject recognition. This paper proposes HarmTSFormer, a two-stage SSVEP decoding architecture. In the first stage, the Dual-Branch Harmonic Encoder (DBHE) constructs harmonic evidence from candidate frequencies and phases. Encoding parameters shared across candidates within each branch enable unified representations of seen and unseen stimuli, while low- and high-order Transformer branches model dependencies among channels, frequency bands, and harmonic responses. Aggregated candidate-set context further informs individual representations. In the second stage, the Evidence-Preserving Aggregation Module (EPAA) combines score normalization with learnable weighting to fuse learned scores and explicit reference-matching scores, integrating EEG response features with reference-matching information. HarmTSFormer uses only source participants' seen-frequency EEG for staged training, without target-user calibration. Evaluations on three public datasets demonstrate its effectiveness under cross-subject, unseen-frequency conditions. Ablation experiments, structural comparisons, and score decomposition analyses further examine the contributions of different information pathways to decoding performance.