Calibration remains a practical barrier for steady-state visual evoked potential (SSVEP) brain–computer interfaces. Task-related component analysis (TRCA) is accurate when several labelled trials per class are available, but its performance degrades when calibration is reduced to one trial and the test block follows a change in recording conditions. We asked whether a fixed modification of ensemble TRCA (eTRCA) could reduce this loss without any per-subject hyperparameter tuning. The proposed SST-eTRCA appends a three-sample delayed copy of each channel, z-scores observations before covariance accumulation, and pools 80 class filters into one projection. On the public 35-subject Tsinghua benchmark (40 classes, six posterior channels), it reached 95.6% with one calibration trial per class versus 93.4% for eTRCA (1400 decisions; McNemar p = 1.5E-5), and 95.0% versus 91.8% without extra calibration. The advantage was largest at 1 s (68.9% versus 63.9%). Under leave-one-subject-out, SST-eTRCA and eTRCA were indistinguishable (83.6% each), and self-training changed accuracy by only 0.1 points (p = 0.75). The gain is therefore specific to within-participant block shift, not to cross-subject transfer. These results define a narrow but useful operating range: a fixed, reproducible decoder for reducing recalibration across blocks, with the negative self-training result serving as a reliability check.