This study explores two zero-training methods aimed at enhancing the
usability of brain-computer interfaces (BCIs) by eliminating the need for a
calibration session. We introduce a novel method rooted in the event-related
potential (ERP) domain, unsupervised mean maximization (UMM), to the fast
code-modulated visual evoked potential (c-VEP) stimulus protocol. We compare
UMM to the state-of-the-art c-VEP zero-training method that uses canonical
correlation analysis (CCA). The comparison includes instantaneous
classification and classification with cumulative learning from previously
classified trials for both CCA and UMM. Our study shows the effectiveness of
both methods in navigating the complexities of a c-VEP dataset, highlighting
their differences and distinct strengths. This research not only provides
insights into the practical implementation of calibration-free BCI methods but
also paves the way for further exploration and refinement. Ultimately, the
fusion of CCA and UMM holds promise for enhancing the accessibility and
usability of BCI systems across various application domains and a multitude of
stimulus protocols.

该研究探讨了两种零训练方法，旨在通过消除校准过程提高脑 - 机接口（BCI）的可用性。研究引入了一种以事件相关电位（ERP）为基础的新方法，无监督均值最大化（UMM），应用于快速编码调制视觉诱发电位（c-VEP）刺激协议中。通过与使用典型相关分析（CCA）的现有 c-VEP 零训练方法进行比较，我们将 UMM 进行了评估。研究结果展示了两种方法在导航 c-VEP 数据集的复杂性上的有效性，强调了它们之间的差异和各自的优势。该研究不仅洞察了无需校准的 BCI 方法的实际实施情况，还为进一步的探索和改进铺平了道路。最终，CCA 和 UMM 的融合有望提高 BCI 系统在各种应用领域和多种刺激协议下的可访问性和可用性。