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.

翻译：基于视觉调制范式的脑机接口（BCI）的最终目标是在无需大量校准负担的前提下实现高速性能。采用宽带白噪声调制的码调制视觉诱发电位BCI（cVEP-BCI）具有多种优势，包括提高通信速度、扩展编码目标能力以及增强编码灵活性。然而，宽带刺激下时空模式的复杂性使得cVEP-BCI在有效识别目标时需要大量校准。因此，在有限校准条件下，cVEP-BCI的信息传输速率通常维持在每分钟100比特左右，显著落后于当前最先进的稳态视觉诱发电位BCI（SSVEP-BCI），后者可达200比特/分钟以上。为在最小化校准情况下提升cVEP-BCI性能，我们设计了一个高效的校准阶段：采用持续时间不足一分钟的短暂单目标闪烁来提取可泛化的时空模式。基于校准数据，我们开发了两种互补方法构建cVEP时间模式：基于刺激序列的线性建模方法与利用跨被试数据的迁移学习技术。结果表明，在一分钟校准条件下，我们实现了250比特/分钟的最高信息传输速率，该性能已证明可与最先进的SSVEP范式相媲美。总之，本研究显著提升了少样本学习条件下cVEP的性能，有望拓展cVEP-BCI的实用性与可用性。