  The ultimate goal of brain-computer interfaces (BCIs) based on visual
modulation paradigms is to achieve high-speed performance without the burden of
extensive calibration. Code-modulated visual evoked potential-based BCIs
(cVEP-BCIs) modulated by broadband white noise (WN) offer various advantages,
including increased communication speed, expanded encoding target capabilities,
and enhanced coding flexibility. However, the complexity of the
spatial-temporal patterns under broadband stimuli necessitates extensive
calibration for effective target identification in cVEP-BCIs. Consequently, the
information transfer rate (ITR) of cVEP-BCI under limited calibration usually
stays around 100 bits per minute (bpm), significantly lagging behind
state-of-the-art steady-state visual evoked potential-based BCIs (SSVEP-BCIs),
which achieve rates above 200 bpm. To enhance the performance of cVEP-BCIs with
minimal calibration, we devised an efficient calibration stage involving a
brief single-target flickering, lasting less than a minute, to extract
generalizable spatial-temporal patterns. Leveraging the calibration data, we
developed two complementary methods to construct cVEP temporal patterns: the
linear modeling method based on the stimulus sequence and the transfer learning
techniques using cross-subject data. As a result, we achieved the highest ITR
of 250 bpm under a minute of calibration, which has been shown to be comparable
to the state-of-the-art SSVEP paradigms. In summary, our work significantly
improved the cVEP performance under few-shot learning, which is expected to
expand the practicality and usability of cVEP-BCIs.
