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.

翻译：本研究探索了两种零训练方法，旨在通过消除校准环节来提升脑机接口（BCI）的可用性。我们将基于事件相关电位（ERP）领域的新方法——无监督均值最大化（UMM）引入快速编码调制视觉诱发电位（c-VEP）刺激协议。我们将UMM与当前最先进的基于典型相关分析（CCA）的c-VEP零训练方法进行对比。对比内容包括CCA和UMM的即时分类以及基于先前分类试验的累计学习分类。研究表明，两种方法在处理c-VEP数据集的复杂性方面均展现有效性，同时凸显了各自的差异与独特优势。本研究不仅为无校准BCI方法的实际应用提供了见解，也为进一步的探索与优化铺平了道路。最终，CCA与UMM的融合有望提升BCI系统在多种应用领域及多类刺激协议中的可及性与可用性。